Telerehabilitation using a 2-D planar arm rehabilitation robot for hemiparetic stroke: A feasibility study of clinic-to-home exergaming therapy

doi:10.21203/rs.3.rs-4693518/v1

Download PDF

Research Article

Telerehabilitation using a 2-D planar arm rehabilitation robot for hemiparetic stroke: A feasibility study of clinic-to-home exergaming therapy

https://doi.org/10.21203/rs.3.rs-4693518/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Aims: We evaluated the feasibility, safety and efficacy of a 2D-planar robot for minimally-supervised home-based upper-limb therapy for post-stroke hemiparesis.

Materials and Methods: The H-Man, end effector robot, combined with web-based software application for remote tele-monitoring were evaluated at homes of participants. Inclusion criteria were: strokes > 28 days, Fugl-Meyer Motor Assessment (FMA) >10-60/66, presence of a carer and absence of medical contraindications. Participants performed self-directed, minimally-supervised H-Man training for 30 consecutive days at their homes, after 2 therapist-supervised clinic on-boarding sessions. Web-based compliance measures were accessed sessions (>20mins/day), training minutes/day and active training hours/30days. Clinical outcomes obtained at weeks 0, 5 (post-training), 12 and 24 (follow-up) consisted of FMA, Action Research Arm Test (ARAT) and WHO-Stroke Specific Quality Of Life (SSQOL). To estimate immediate economic benefits of the home-based robotic therapy, we performed cost-effectiveness analysis (CEA), followed by budget impact analysis (BIA).

Results: Altogether, all 12 participants completed H-Man at home without adverse events; 9 (75.0%) were males, mean (SD) age, 59.4 years (9.5), median (IQR) stroke duration 38.6 weeks (25.4, 79.6) baseline FMA (0-66) 42.1 ±13.2, ARAT (0-57) 25.4 ±19.5, SSQOL (0-245) 185.3 ±32.8. At week 5 follow-up, mean (SD) accessed days were 26.3 days ±6.4, active training hours of 35.3 hours ±14.7/30days, or ~6 days/week and 77 training minutes ± 20.9/day were observed. Significant gains were observed from baseline across time; ΔFMA 2.4 at week 5 (FMA 44.5, CI 95% 39.7 – 49.3, p < 0.05) and ΔFMA 3.7 at week 24 (FMA 45.8, CI 95% 40.5 – 51, p < 0.05); ΔARAT 2.6 at week 5 (ARAT 28.0, CI 95% 19.3 – 36.7, p < 0.05), and ΔARAT 4.8 at week 24 (ARAT 30.2, CI 95% 21.2 – 39.1, p < 0.05). At week 5 follow-up, 91% of participants rated their overall experience as satisfied or very satisfied. Incremental CEA observed savings of -S$144/per cure over 24 weeks, BIA – potentially 12% impact reduction over five years.

Conclusions: This study demonstrates the feasibility, acceptability, safety, clinical efficacy and cost-effectiveness of a home-based, web-enabled telemonitored carer-supervised robotics-aided therapy.

Telerehabilitation

Tele-monitoring

Stroke

Robotics-assisted therapy

End effector robot

Upper limb

Cost effectiveness

Stroke is a disorder characterized by significant impairment of sensorimotor and cognitive functions. Globally, stroke is the second-leading cause of death, accounting for 11.6% of total deaths and the third-leading cause of death and disability combined, accounting for 5.7% of total disability-adjusted life years (DALYs) [1].

In particular, hemiparetic weakness is common after stroke [2,3], affecting 70% - 80% of stroke survivors. In terms of upper extremity (UE) motor function, only 39% of stroke survivors achieve useful upper limb function 6 months after stroke, despite best rehabilitation efforts [4,5]. Thus, stroke should be regarded as a long-term condition requiring continuing support [6].

Stroke rehabilitation aims to maximize functional independence and improve the patient’s quality of life through a combination of reduction of impairment and learning of compensatory motor strategies [7].Greater functional independence in the patient leads to reduced caregiver burden, better quality of life and potentially lower costs of care [8]. Current evidence in stroke rehabilitation emphasizes the need for repetitive, intensive and adaptive task-specific UE training to facilitate motor relearning and neuroplasticity [9,10].

Upper-limb Robotics-Assisted Therapy (RAT) can deliver task-specific, repetitive, intensive UE exercises safely with comparable clinical outcomes and improved neuroplasticity [11–13]. Current studies on RAT and dose-matched conventional therapy show comparable effects on improving motor outcome with high levels of safety and acceptability [14–16]. To date, the largest trial on RAT involving 770 randomized patients, found reduced-supervision RAT to be equally effective as enhanced upper limb therapy or usual care [17]. Thus, RAT provides a potential solution to provide quality-ensured upper limb intensive therapy and decrease therapists’ workload [12,18].

The development of table-top, portable, upper limb end effectors has afforded innovative, effective, low-cost solutions comparable to usual clinic therapies. These devices have the potential to bridge a variety of gaps in accessing clinic RAT such as scheduling, physical or social barriers, pandemic-related lockdowns, through decentralized and minimally supervised home-based therapy[14,19–22].

It had also long been assumed that stroke patients reach a plateau in their recovery within 6 months of their stroke. However, several studies challenge this assumption. A proportion of interventions delivered > 6 months post-stroke demonstrated a positive benefit for individuals in the chronic stage of stroke [23]. Ward et al. reported results of a clinic-based UE rehabilitation program consisting of 90 hours over 3 weeks, for chronic stroke survivors (median time 18 months post-stroke) with severe UE disability (mean Fugl-Meyer-Motor Assessment (FMA) score 26/66), who achieved clinically significant gains of 42% in motricity and 50% gains in motor function, which persisted for 6 months [24,25].

These findings suggest that given the length of time needed for post-stroke UE recovery in relation to motor and functional benefits, substantial provisions should be made for post-hospitalization rehabilitation to be continued for months to years after the initial stroke. However, challenges remain in matching therapy provision to optimize recovery or neuroplasticity in the poststroke subacute to chronic phase, in large part related to healthcare resource limitations and various barriers. For example, a 2014 Singaporean study found that, in general, post-hospital rehabilitation attendance was low [26]. While 87.1% of the patients viewed rehabilitation as beneficial, but overall longitudinal attendance rate fell from 100% as inpatient to 20.3% at 3 months, 9.8% at 6 months, 6.3% at 9 months and 4.3% at 12 months. Reasons for this included physical and social barriers, which were high initially, but decreased with time, while the prevalence of financial and perceptual barriers increased with time [26].

Home-based training and telerehabilitation combined with technology deployment at home or nursing facilities are various methods which can increase therapy delivery without over-burdening healthcare manpower. The latter could be achieved by leveraging rehabilitation technology, internet-enabled cloud services and healthcare professional surrogates through upskilling of caregivers and patients’ families [19,22,27].Also, home-based therapy combined with telerehabilitation and technology are potential methods to optimize therapy intensity and circumvent traditional barriers to access, such as transportation, scheduling and staff availability, extending UE rehabilitation beyond the clinic [19]. It has shown largely equivalent efficacy between clinic and purposively-designed, self-managed UE home programs [28].

A telerehabilitation system typically involves education, exercise via exergames, training or support delivered either synchronously or asynchronously, for example, via a video-conferencing system or internet-enabled software, allowing therapists to monitor patients at home remotely via the telerehabilitation platform [29]. Stroke patients who completed home-based telerehabilitation achieved outcomes equal or better to those from conventional care during the first 3 months of stroke [29,30]. A large randomized controlled trial (RCT) of 124 subacute and chronic strokes with moderate to severe UE impairment showed that 36 hours of 70 minutes each of telerehabilitation, combining unsupervised and supervised via computer games, was non-inferior to dose-matched clinic-based rehabilitation, with both groups achieving 7.86 - 8.36 FMA gains after 6 weeks of training [31].

We aimed to evaluate the feasibility, safety, and efficacy of carer-minimally supervised RAT using a portable arm robot within homes of patients, supported by a web-based platform and remote telemonitoring, combined with longitudinal assessment of standardized outcome measures and patient-reported outcomes. Secondarily, to evaluate cost-effectiveness and budget impact of RAT at home.

2.1. Overview of methods

An integrated robotic therapy platform is demonstrated allowing patients to train with minimal supervision at home preceded by prior in-clinic eligibility screening followed by 2 clinic on-boarding sessions. The integrated solution consists of the following components:

· Robotics: H-Man [32] is a portable, 2D planar end-effector robot designed for upper-limb therapy (Fig. 1a). The system provides smart physical human-robot interaction (haptics) in substitution of physical interaction with a human therapist.

· Telerehabilitation: Remote monitoring via software and internet access, allowing clinician remote telemonitoring.

· Clinical protocol. These included 1 session of in-clinic supervised conventional occupational therapy (COT) and 10 telemonitoring sessions over 30 days during home-based H-Man training.

· Clinical trial description. We conducted a prospective, pilot, single-arm trial of hemiparetic stroke patients with independent outcomes assessment and longitudinal follow up till 24 weeks of baseline. The goal of this study was to determine the feasibility, safety and acceptability of implementing a clinic-to-home rehabilitation pathway using RAT. H-Man, a robotic device was deployed in all participants’ homes to perform home-based RAT.

In particular, the following outcomes were evaluated:

1) Primary outcomes of participants’ compliance with the therapy plan, were defined in two ways. Firstly, we defined as an "active day" any day within the 30-day therapy program in which a participant training was logged into the robot’s software for at least 20 minutes. Secondly, we defined as “active hours/30 days” or “active minutes/day”, the total time spent, removing idling time of the robotic handle.

a) Patterns of participant usage per day according to time stamped on the web application.

b) Safety data with regards to dropouts during 30-days training period or follow-up phase, increased arm pain or spasticity from baseline scores.

c) Individual participant subjective ratings using a derived assessment tool for H-Man.

2) Secondary outcomes of clinical efficacy at the impairment, function, and health-related quality of life (Hr-QOL) using standardized outcome scales and their durability at the end of home RAT at week 5 and at 24 weeks (follow-up period of 19 weeks).

The study’s hypothesis was that telerehabilitation using a carer-minimally-supervised H-Man robot at home for 30 days and clinic remote telemonitoring by occupational therapists (OT) would achieve the following outcomes:

1) 75% of sample achieving an active day defined as any log-in of >20 minutes continuously/day.

2) <10% drop out rate of enrolled participants during the 30-day H-Man home training period.

3) < 10% of participants’ adverse events related to H-Man training such as arm pain, shoulder pain, increased spasticity on clinically measured scales by independent assessors.

Institutional ethical approvals were obtained by the National Healthcare Group, Domain Specific Review Boards (NHG-DSRB 2021/00156) prior to participant recruitment and study procedures. The study was conducted in accordance with the Declaration of Helsinki, which governs ethical principles for medical research involving human subjects. All participants signed written informed consent prior to enrolment. The study was registered with www.clinicaltrials.gov (NCT: 05212181) [33].

Retrospective data related to participants’ demographic, acute stroke characteristics and individualized billed cost data were extracted from institutional electronic medical records. All other clinical or robotic metric data were prospectively collected.

2.2. Hardware

The upper-limb rehabilitation robot employed in this study is shown in Fig. 1a. H-Man is a portable, planar end-effector device designed to help train arm movements [34]. The robot is essentially a powered, cable-driven differential mechanism. The mechanism design provides the following advantages:

• High back drivability: Back drivability refers to the ease with which the user can move the handle in the absence of motor actuation. Compared to other robot designs, the inertia and friction felt by the user’s hand when moving the handle are minimal. In this way, the user concentrates on performing the training tasks rather than overcoming the resistance of the mechanism.

• Isotropy: Unlike serial-link manipulators, the inertia and friction perceived by the user are constant for all positions of the handle.

• Optimal workspace dimensions: The mechanism offers one of the highest ratios of workspace area to total mechanism area (workspace is the set of all possible positions of the handle on the vertical plane). This makes it possible to use the device on a typical household table.

H-Man can provide end-effector forces of up to 23 Newtons in any specified direction of the planar workspace to collaborate in the rehabilitation task. Previous clinical studies with H-Man can be found in Campolo et al., 2014; Hussain et al., 2016 and Budhota et al., 2021. [14,20,34].

2.3. Robotic intervention (exergames)

Therapy sessions with H-Man involved the participant performing a series of game-like training exercises or ‘exergames’ provided by the robot’s software. The exergame’s graphic user interface provides the user with a virtual manual task to execute, such as capturing fish in a pond, serving meals to customers, etc. The robot interacts physically with the user by exerting controlled forces on the handle. Depending on the type of task, these forces can either help the user in completing the required movements or create a challenge, such as adding resistance or introducing perturbations. In some games, the control software features an adaptive component that automatically adapts the intensity of the therapy to the patient’s current level of recovery. Table 5 and Table 6 in Appendix 2 present a summary of the exergames employed in this study.

Exergames are prescribed by an OT and tailored to each participant’s needs; working towards improving arm coordination, strength and/or agility. Accordingly, games with assistive, resistive and/or perturbative forces are selected and modified during the course of training as appropriate.

2.4. Remote monitoring software

The robotic system is controlled by a software application called the CARE Platform [35]. The software features a remote monitoring component capable of linking up the supervising clinician with one or several patients receiving robotic therapy in their homes (Fig. 2). In compliance with the institution’s Medical Devices and Operational Technology Security (MDOTS) [36], no personal identifiers (name, identity numbers, addresses) were stored in the robot or web-based platform which was not connected with the healthcare institution’s network and H-Man robot external USB ports were disabled.

The software’s communication framework featured encrypted transmission of training data from the H-Man robot to a secure database, and generation of data analysis and progress reports, allowing remote access by clinicians with secure log in passwords to view and manage participants’ therapy schedules and generate reports remotely.

2.5. Study setting

The study was conducted from 3 March 2022 to 1 September 2023 at the Tan Tock Seng Hospital, Clinic for Advanced Rehabilitation Therapeutics (TTSH-CART) in Singapore, an ambulatory rehabilitation facility providing comprehensive medical rehabilitation consultations and multi-disciplinary rehabilitation therapies, incorporating various rehabilitation technologies (e.g., robot-aided therapies, virtual reality training, neuromuscular electrical stimulation etc.). TTSH CART is directly linked to Tan Tock Seng Hospital (TTSH) Rehabilitation Centre, a 95-bed inpatient tertiary rehabilitation unit providing acute inpatient neurorehabilitation programs.

2.6. Study participants

The majority of participants had completed inpatient rehabilitation at TTSH Rehabilitation Centre and were recruited consecutively according to the following study inclusion criteria; first-ever clinical stroke (ischaemic or hemorrhagic) confirmed by admitting doctors and CT, CT angiography or MRI brain imaging, aged 21 to 90 years, duration of > 28 days post-stroke, upper limb motor impairment measured with Fugl-Meyer Motor Assessment scale (FMA) scale between 10 - 60/66 [25], presence of stable home situation and a carer to supervise home-based RAT, Montreal Cognitive Assessment (MoCA) score > 21/30 and ability to understand purpose of research [37]. Exclusion criteria are provided in Appendix 1.

2.7. Study protocol overview

The protocol for the home-based training and follow-up is shown schematically in Fig. 3. Following eligibility screening and signed informed consent, 2 clinic onboarding sessions of 90 minutes each were conducted within a week by an OT for both the participants and their appointed carer. This was followed by a single home visit by the vendor to deliver and set up the H-Man at the participants’ homes. Simultaneously, an OT was present at this home visit for appropriate interfacing of the participants to H-Man, reinforcement of H-Man training, safe operations and handling of the robot. From the next day, H-Man home-based training was commenced for 30 consecutive days. The H-Man was then retrieved from the participants’ homes.

At week 5 after V1, participants returned to the clinic for 1 session of clinic-based OT. Follow-up assessment sessions using standardized outcome measures were conducted in the clinic on weeks 5, 12 and 24 (Fig. 3). All T0-T4 assessments and up to 10 remote telemonitoring sessions were conducted by an OT.

2.7.1. Description of in-clinic phase

Following screening and informed consent, each participant was assigned a unique research identifier code, which was used in data collection forms, the clinic and home H-Man robots and a web-based platform to identify participants. Participants were then assessed at baseline by an OT using the above outcome measures (T0, visit 1), followed by a 90-minute clinic onboarding session at TTSH-CART. The main purpose was to introduce participants to H-Man robot training. Particular attention was paid to proper trunk posture and positioning in height-adjustable chairs with appropriate hemiplegic shoulder positioning and hand straps to the robotic handle as needed. A second 90-minute session (visit 2) was conducted within the same week to familiarize participants to the various exergames, training schedules and progression and to train their carers on proper operational handling, safety aspects and progression of training on the H-Man robot.

Subsequently, visit 3 occurred at the participants’ homes with the concurrent delivery and installation of the H-Man robot by the vendor and training set up by CART OT over 90 minutes (Fig. 3). The goal of this visit was to ensure continuity of ergonomic positioning of the participant, which was previously established during the prior 2 clinic onboarding sessions; also, supervision or manual assistance from carers or next of kin as needed for proper positioning at the H-Man or for adjustment of controls; and revision of safety and trouble-shooting protocols by participants and carers. Participants were given contact numbers to short message or contact OTs or vendor in case of physical or technical difficulties respectively. A paper record was also provided for manual logging of dates, start and end times of each of the training sessions as a consistency countercheck against the web-based cloud data.

2.7.2. Home training phase

Participants were instructed to perform daily home-based H-Man training for the next 30 days, starting at 20-30 minutes per session daily and progressing with rest breaks as needed to 60 minutes/day at the end of the first week and further increasing to 120 minutes daily in distributed sessions by the end of the second week. OTs did not perform synchronous tele-monitoring facing the participants during the 30-day home training phase.

Remote asynchronous tele-monitoring via the web-based cloud platform was performed by OTs in the clinic for 10 minutes each, up to 10 sessions over 30 days (i.e., 2-3 times per week). This involved accessing the cloud data and participants’ performance (log-in duration, dates, times via a graphical interface). The first remote monitoring session occurred 24 hours after visit 3 (delivery and set-up of the H-Man) and proceeded as per protocol at 2-3x/week up to 10 sessions/30days. Telephone calls or short messaging from OTs to participants/carers were on an as-needed basis, when the following situations were encountered: absence of web-based cloud activity noted for > 24 hours initially, intermittent or poor compliance (i.e., irregular or infrequent log-in <20 minutes each time) or failure to progress training duration to 60 minutes/day by day 14/30 days.

At the end of 30 days, the H-Man robot was retrieved from participants’ homes by the vendor.

2.7.3. Follow-up phase

These consisted of 3 clinic visits of 60-90 minutes each (visits 4-6). These included 1 session of independently rated outcome measures and functional retraining by OT at week 5 (T1, visit 4), and 2 further follow-up outcome measures, assessed by OT at weeks 12 (T2, visit 5), and weeks 24 (T3, visit 6). Functional retraining included ranging and mobilization followed by guided practice of reach coordination and grasp/release functions utilizing neuro-facilitatory handling techniques such as the Bobath Concept and Neurodevelopmental Treatment, with Task-oriented Training [38,39].

Participants were discharged from the study at week 24 upon completion of all outcome measures.

2.8. Outcome measures

Outcomes measures were classified into 4 main groups: (i) compliance with therapy plan and safety data, (ii) participant subjective ratings, (iii) standardized clinical outcomes and (iv) cost-effectiveness data.

2.8.1. Therapy plan: compliance and safety data

Training compliance included number of active days, hours/30 days and active minutes/day were obtained from the data uploaded to the cloud server from H-Man training sessions. In addition, participants filled out manual logs with individual log in and out times at home, which were used as a countercheck. Safety data included dropout rate during the 30-day H-Man home training period and adverse events related to H-Man training, such as arm pain, shoulder fatigue or increased spasticity on clinical spasticity scales which were recorded at week 5 (T1, Visit 4).

2.8.2. Participant subjective ratings

Patient reported outcome measures (via standard questionnaire), where participants rated on a Likert scale [40] of 1-5, with 1 being strongly disagree and 5 being strongly agree on their home-based experience with H-Man. The questions (1-7) were as follows:

1. It is easy to learn how to use the system.

2. The set-up was comfortable.

3. The training was easy to complete at home.

4. The training was not boring.

5. The training was useful for exercising my arm.

6. The home robot training should be part of standard therapy.

7. Overall satisfaction scale.

2.8.3. Standardised clinical outcomes

The following clinical outcomes were measured by independent OTs not involved in visits 1-3 at T0,1,2,3.

· Fugl-Meyer Motor Assessment (FMA) is a widely used quantitative measure of motor impairment to evaluate upper-limb recovery [25]. It is scaled from baseline measurement in week 0 of the trial and score ranges from 0 being the minimum to the maximum score of 66 points.

· Action Research Arm Test (ARAT) is a 19-item observational measure of upper-extremity performance that consists of 4 sub-tests (grasp, grip, gross and pinch movement). Each task performance is rated on a 4-point scale ranging from 0 (no movement) to 3 (normal movement). Scores from each task will be summed, with a minimum total score of 0 to a maximum score of 57 [41,42].

· Affected hand grip strength was measured using Jamar Dynamometer (kg) using the mean reading of 3 attempts [43].

· The Stroke Specific Quality of Life Scale (SSQOL), an instrument intended to measure the quality of life specific to stroke patients [44]. The instrument consists of 49 items within 12 domains such as family roles, self-care and mobility. Each item is scored on a 5-point Likert scale [40] from 1-5, with a minimum total score of 49 and a maximum of 245. Higher scores imply higher QOL.

· In terms of participant safety monitoring, these included clinical measures of hemiparetic limb spasticity of shoulder adductors, elbow flexors, wrist and finger flexors using the Modified Ashworth Scale scores (MAS) [45] and visual analogue scale pain scale (VAS 0-10) [46] (Appendix 4, Table 7).

All participant demographic and clinical data were collected and managed on the RED-Cap electronic tool hosted at the National Healthcare Group [36].

2.8.4. Statistical analysis

The sample size analysis recommended for pilot studies ranges between 10 to 30. Thus, a sample size of 10 was planned. Factoring in a ~20% drop out rate (~2 subjects), the total sample size was then 12.

Modified intention to treat analyses was performed [47]. The analysis of variance (ANOVA) was conducted to examine changes in different clinical scales with repeated measures. For each subject, data collected from the proposed total 24-week follow-up study at different time points, at weeks 0, 5, 12 and 24, may exhibit association. Statistical methods, such as the linear mixed model and the generalized estimating equations (GEE) were employed for longitudinal analysis to provide more efficient inference with intervention as a covariate. Other covariates in the longitudinal analysis included participants’ baseline characteristics such as age, sex, stroke subtype, severity, location and initial motor impairment (total FMA score), etc.

Final adjusted clinical effect sizes (FMA) COT, RAT at clinic and RAT at home (using H-Man) were calculated using multivariate mixed random effect models with unstructured covariances and sandwich regressor (Robust Variance) option to take into account for quantifying heterogeneity within subject variability for repeatedly measured FMA scores over time; unstructured covariance matrix which provide a flexible framework for modelling the correlation structure of the data, while the sandwich estimator helped to correct for any potential misspecification of the covariance matrix.

We also included all other clinically significant covariates such as age stroke type, duration, affected side in the model for statistical adjustment. These adjusted variables were significant in the univariate model as well as considered to be clinically significant variables. A two-sided p-value of less than 0.05 was considered a statistical significance level.

2.8.5. Cost-effectiveness analysis (CEA)

The CEA [48] was conducted based on societal (strokestroke survivors’ and hospital’s) perspectives. The time horizon of the analysis was set at ~1 year in line with the study duration. In this initial analysis, we quantified and compared the costs of each intervention at the follow-up (week 24), COT, RAT at clinic and RAT at home (using H-Man) and their corresponding effectiveness measures, such as clinical FMA outcomes. Cost data for COT, RAT at clinic were retrospective, billed data for each participant where available. RAT at home billed data was collected prospectively for all 12 participants. All costs were estimated in Singapore dollars (S$). Details on the estimation of direct and indirect medical costs for all interventions are provided in Appendix 3. FMA scores for COT and RAT at clinic were obtained from earlier conducted clinical trial in the same clinic as the current study [14]. Adjusted clinical effect sizes (FMA) for COT, RAT at clinic and RAT at home were calculated using multivariate mixed random effect models and clinically important variables were adjusted in the models (more details in section 2.8.4). CEA was carried out using model-based, estimated individual predicted clinical effect sizes, and direct, indirect, and total costs for 3 unique treatment pathways.

Incremental Cost-Effectiveness Ratio (ICER) was calculated using the following formula (1):

The ICER indicates the additional cost incurred to attain an additional unit of effectiveness with the new intervention when compared to the alternate choice or comparator.

2.8.6. Budget impact analysis (BIA)

The BIA [49] aimed to estimate the potential impact of increased uptake of a new intervention (RAT at home) compared to the current model—only COT. BIA used Singapore's national perspective and a five-year time horizon. To estimate the annual number of stroke survivors eligible for post-stroke rehabilitation, we used national statistics reported by the Singapore Stroke Registry [50] and the Ministry of Health data [51].

3.1. Participants and study

Altogether, 12 participants were enrolled from an initial sample of 20 outpatients. Fig. 4 shows the participant recruitment flow diagram. All 12 (100%) participants completed initial on-boarding phase of 3 visits (V0-3) and 30 days of minimally supervised H-Man at home training without adverse events or training-related side effects such as pain, increased spasticity or cybersickness. Ten out of 12 participants (83.3%) completed the 24 weeks study while 2 out of 12 (16.7%) were lost to follow-up during the follow-up phase (P06, P12).

Table 1 shows the 12 individual participants’ baseline data. Altogether, there were 9 (75%) males and the mean (±SD) age 59.4 (±9.5) years with an equal number of right (6) and left (6) hemiplegic participants. All participants were in chronic phase of stroke, with considerable variation in the individuals’ time after stroke - median duration 38.6 weeks (IQR 25.4, 79.6).

The baseline total FMA score was mean (SD) 42.1 (±13.2) with subtotal FMA (proximal) of 28.9 (±6.5) and subtotal FMA (distal) of 13.2 (±7.0). Mean (±SD) total ARAT (0-57) was 25.4 (±19.5) while subtotal ARAT scores were 8.1 (±7.1) grasp, 5.6 (±4.7) grip, 5.5 (±6.8) pinch and 6.3 (±1.8) gross. This indicated a chronic population with moderate to severe poststroke arm motor impairment.

Mean (±SD) hemiplegic hand grip strength was 7.8 (±2.8) kg, SSQOL (0-245) 185.3 (±32.8) (See Appendix 4 for baseline spasticity).

Table 1 Baseline individual demographic and clinical characteristics (N=12)

Subject No (P-n)	Group	Age (Years)	Sex/Race	Time since stroke (weeks)	Stroke subtype	FMA/66	ARAT/57	HGS (affected) /kg	Dominant hand trained (Y/N)	SSQOL/245	COT (Y/N)
001	1	75	M/Chinese	89.1	Infarct	15	3	6.2	Y	173	N
002	1	52	M/Burmese	501.0	ICH	33	12	6.2	Y	184	N
003	1	54	M/Chinese	51.1	Infarct	46	24	13.4	Y	197	Y
004	1	56	F/Japanese	177.4	Infarct	36	6	5.9	N	155	Y
005	1	58	M/Chinese	13.4	Infarct	53	38	6.9	N	237	Y
006	1	63	M/Indian	38.3	Infarct	46	22	8.5	Y	177	Y
007	2	54	F/Chinese	41.6	Infarct	53	48	8.2	N	221	Y
008	2	50	M/Chinese	33.6	ICH	52	45	10.3	N	122	N
009	2	76	F/Chinese	22.3	Infarct	56	55	11.2	Y	214	Y
010	2	46	M/Chinese	38.9	Infarct	31	4	3.4	N	200	Y
011	2	62	M/Chinese	32.1	Infarct	56	43	8.4	N	196	Y
012	2	67	M/Caucasian	23.1	ICH	28	5	5.4	Y	147	N

Legend: M: Male, F: Female, ICH: Intracerebral Haemorrhage, FMA: Fugl-Meyer Assessment scale score, ARAT: Action Research Arm Test, HGS: Hand Grip Strength；SSQOL: Stroke specific Quality of Life, COT: receiving conventional occupational therapy during 24 weeks study involvement

Group 1 consisted of 6 participants (P01-P06) who trained with 3 different exergames at home (Appendix 2, Table 5). Halfway into the study period, a further 5 exergames had been developed by the software developers. Group 2, also consisting of 6 participants (P07-P012; group 2), trained with a total of 8 exergames at home (Appendix 2, Table 6).

3.1.1. Follow-up phase

During the 19-week follow-up phase, 2 out of 12 participants (16.7%) were uncontactable (P06, P12).

Two participants (P005, P010) encountered minor equipment malfunctions, rectified expediently with replacement robots. Two (P001, P008) had software malfunctions, rectified virtually.

3.2. Training compliance obtained from web-based platform data

3.2.1. Training time and compliance with the training plan and training patterns

In this study, we defined "active days" as days in which a participant trained at home and logged into the CARE platform at least once for 20 minutes. The number of active days per participant (mean, ± SD) was 23.0 (± 7.9) for Group 1, 29.7 (± 0.8) for Group 2 and 26.3 (± 6.4) for the combined groups over a total of 30 days (Fig. 5a). Notably, Group 2, using 8 exergames at home, reported ~100% compliance with the goal of daily training during the 30-day intervention period. Such a level of engagement represents a substantial increase with respect to clinic-based COT, typically not more than twice weekly or a total of 8 hours/month.

The total training time (mean, ± SD) defined as “active hours” per participant during the home trials, removing robotic idling (non-training) time over 30 days, was 28.4 (± 15.1), 42.2 (± 11.6), 35.3 (± 14.7) hours for Group 1, Group 2, and combined groups respectively (Fig. 5b). This too, is a marked average increase in total training time by 3-5-fold respectively, in comparison with clinic-based COT, typically about 8 hours/month.

On active daily minutes, the participants averaged (± SD) 68.9 (± 21.4), 85.3 (± 23.4), and 77.1 (± 23.0) total minutes of training for Group 1, Group 2, and the combined groups respectively (Fig. 5c). There was a sizable proportion of training sessions exceeding 60minutes/day of training, with Group 2 averaging ~90 minutes of training daily. Usual COTS is capped at 60minutes/session in our healthcare institution.

No participant reached the advised training duration limit of 120 minutes/day.

Participants in both groups divided their training into ~2 or more sessions per day (Fig. 6a), thereby taking advantage of the flexibility and convenience of home-based training at home. Robotic non-training or idling time, for example, pausing to decide which game to use next, or simply resting, was also documented. Comparing groups 1 and 2, 20% less non-training time was documented in group 2, with an overall total of 22% of training time spent idling (Fig. 6b).

Fig. 8 shows the participants’ self-reported outcomes. In general, all 12 participants felt that the exergames was early to learn, 10/12 were overall quite or well satisfied with the overall experience and deemed it to be useful with a comfortable setup; and 4/12 opined that the exergames could be boring. Overall, 66.7% desired that H-Man at home was part of their standard treatment. Individual comments recorded participants’ positive feedback of training at home and motivation related to VR games while some negative feedback was related to robot dimensions, software glitches and stress of training intensity (see Appendix 4 for individual recorded comments from participants).

3.3. Clinical outcomes summary

For each participant, the clinical outcomes were measured via assessments post-trial on weeks 5, 12 and 24. Table 2 provides a summary of the clinical outcomes.

Table 2 Summary data of clinical outcomes by timepoint

Variables, Mean (CI 95% or SD)/ time point	T0 Week 0	T1 Week 5	T2 Week 12	T3 Week 24
FMA/66	42.1 (37.2 - 47)	44.5 (39.7 - 49.3)	46.4 (41.2 - 51.7)	45.8 (40.5 - 51)
ARAT/57	25.4 (16.5 - 34.3)	28.0 (19.3 - 36.7)	28.2 (19.6 - 36.7)	30.2 (21.2 - 39.1)
HGS (Affected) (kg)	7.8 (2.8)	8.6 (2.2)	9.3 (2.3)	10.4 (3.8)
SSQOL/245	185.3 (172.3 -198.2)	190.4 (177.8 - 203)	191.7 (179.7 - 203.6)	202.4 (191 - 213.8)

Legend: FMA: Fugl-Meyer Assessment scale score, ARAT: Action Research Arm Test, HGS: Hand Grip Strength; SSQOL: Stroke specific Quality of Life

Fugl-Meyer Assessment (0-66). Significant FMA gains were observed across all timepoints and sustained beyond training phase to week 24 (Fig. 9); ΔFMA of 2.4 at week 5 (FMA 44.5, CI 95% 39.7 - 49.3, p < 0.05), from baseline (FMA 42.1, CI 95% 37.2 - 47), ΔFMA 4.3 at week 12 (FMA 46.4, CI 95% 41.2 - 51.7, p < 0.05) and ΔFMA 3.7 at week 24 (FMA 45.8, CI 95% 40.5 - 51, p < 0.05).

ARAT (0-57). Similarly, significant ARAT gains were observed across time and sustained beyond training phase to week 24 (Fig. 10); ΔARAT 2.6 at week 5 (ARAT 28.0, CI 95% 19.3 – 36.7, p < 0.05), from baseline (ARAT 25.4, CI 95% 16.5 - 34.3), and ΔARAT 4.8 at week 24 (ARAT 30.2, CI 95% 21.2 – 39.1, p < 0.05).

WHO-SSQOL (0-245): Significant gains were observed from week 12 to 24 (Fig. 11); ΔWHO-SSQOL 17.2 at week 24 (WHO-SSQOL 202.4, CI 95% 191 - 213.8, p < 0.05) from baseline (WHO-SSQOL 185.3, CI 95% 172.3 – 198.2).

Affected hand grip strength (HGS)(kg): Significant gains were observed across time; ΔHGS 0.8 at week 5 (HGS 8.6, ± 2.2, p < 0.05), from baseline (7.8, ± 2.8), and ΔHGS 2.6 at week 24 (HGS 10.4 ± 3.8, p < 0.05). See supplementary Tables 8, 9, 10, 11, and 12 for FMA, ARAT, HGS (affected), WHO-SSQOL and VAS pain individual scores in Appendix 4.

3.4. Health-economic outcomes summary

Cost savings were observed for (S$ 2,416.9) H-Man at home compared to (S$ 3,191.0) COT (control) (p > 0.05) and (S$ 3,282.1) RAT in clinic. This was mainly related to the lower mean direct cost of H-Man at home. Table 3 describes the breakdown costs for each intervention.

Table 3. Mean direct, indirect and total cost of clinic-based conventional occupational therapy (COT), RAT at clinic and H-Man at home

Mean (SD) S$	COT matched to RAT at clinic (Control)	RAT at clinic	H-Man at home (Current intervention)
Total (A+B)	3,191.04 (1,258.50)	3,282.14 (1,386.05)	2,416.92 (253.26)
(A) Direct costs	2,826.12 (1,173.91)	2,917.22 (1,312.51)	2,187.61 (262.62)
Programme cost	2,826.12 (1,173.91)	2,917.22 (1,312.51)	2,050.39 (232.71)
Telemonitoring cost	0.00	0.00	137.22 (83.99)
(B) Indirect costs	364.92 (125.34)	364.92 (125.32)	229.31 (53.56)
Waiting Time	0.00	0.00	29.53 (55.20)
Transportation	364.92 (125.34)	364.92 (125.32)	92.03 (35.30)
Home related furniture	0.00	0.00	107.75 (30.75)
Legend: S$ Singapore dollars

Clinical outcome - the adjusted predicted mean (± SD) values of FMA for H-Man at home, COT and RAT at clinic were 45.8 (± 14.1), 40.4 (± 11.6) and 45.3 (± 11.4), respectively. As shown in Fig. 12, CEA comparing H-Man at home with COT demonstrated that, H-Man at home resulted in a positive incremental effect of ΔFMA +5.4 with a negative incremental cost-effectiveness ratio (ICER) of S$ -143.73 per cure. These results indicated that H-Man at home was cost-effective.

In Singapore, assuming a national perspective, with an annual 4% increase in stroke cases [50], a first-year survival rate of 75% [52] and referral for rehabilitation services of 47.2% [51], the cost of using only COT by 19,842 stroke survivors could reach S$ 63,316,616 over five years. As shown in Table 4, a stepwise increase in uptake of H-Man at home could reduce the annual budget impact from 5% to 19%, on average, 12% of the total cost.

Table 4 Budget impact analysis of the implementation of Home-based Telerehabilitation over five years in Singapore

Year	First year stroke survivors	Eligible stroke survivors	Only clinic-based COT (Control)		Combined use of H-Man at home and COT		Budget Impact (Combined use of H-Man at home and COT vs use of only clinic-based COT)
Year	First year stroke survivors	Eligible stroke survivors	Users of COT (%)	Total cost (S$)	Users of H-Man at home (%)	Total cost (S$)	Total cost difference	Total (%) difference
2024	7,761	3,663	100	11,688,780	733 (20)	11,121,659	- 567,120	5
2025	8,072	3,810		12,157,862	1,143 (30)	11,273,043	- 884,819	7
2026	8,395	3,962		12,642,900	1,981 (50)	11,109,369	- 1,533,532	12
2027	8,731	4,121		13,150,276	2885 (70)	10,917,172	- 2,233,104	17
2028	9,080	4,286		13,676,797	3,429 (80)	11,022,495	- 2,654,303	19
Sum	42,038	19,842		63,316,616		55,443,738	-7,872,878

4.1. General summary of study protocol, target population, safety and usability data

The aim of this study was to evaluate the acceptability, safety, clinical efficacy and cost-effectiveness of a web-enabled telemonitored carer-supervised RAT at home using a 2D planar end effector robot, H-Man. This was the first time H-Man training at home (H-Man at home) was deployed in home settings with carer supervision with longitudinal follow-up over 24 weeks using multimodal outcomes including cost-effectiveness analysis.

Study inclusion criteria were purposively broad (FMA 10-60) to allow assessment of a wide range of participant suitability to allow generalization to the stroke population. In this study, the lower FMA limit was 10 points below that for the first RCT for in-clinic H-Man training, which included FMA ranges 20 -50/66 [14], as facilitated by passive arm supports to support weak proximal UE as needed. Moderate to severely impaired UE (mean baseline FMA of 45, range 15-56/66) were thus feasibly trained without increased hemiplegic shoulder pain during the home training phase. This was similar to a previous large RCT by of activity-based training for motor function comparing home-based telerehabilitation vs in-clinic rehabilitation (FMA 22-56/66) and the mean baseline FMA scores were comparable for telerehabilitation groups (our 42.1 vs Cramer et al.’s 42.8) [31].

COT was provided only once during the 5-week training period for functional training as the main purpose of the study was to evaluate usability aspects of the H-Man at home and telemonitoring platform, patterns of self-driven practice at home and clinical efficacy and cost-effectiveness. Notably, 8/12 (67%) participants had concomitant in-parallel COT as part of usual COT over 24 weeks, and this was not disrupted during the 19-week follow-up period. In general, the clinic’s standard-of-care COT intensity is 2 times/month.

OTs did not exceed the planned 10 sessions of 10 minutes of telemonitoring for any of the 12 participants. (2-3x/week). They also opined that telemonitoring frequency could be reduced by 50% to 4 weekly sessions in future, as cloud data could be readily accessed for remote monitoring. Moreover, participants could either call or short message should they encounter problems.

1.10.1. Sample baseline characteristics

In terms of participants’ ages, our mean age of ~60 years was a decade younger than Singapore’s mean age of stroke of 69.8 years, with a predominant younger stroke population with only 2/12 aged >65 years [50], but of similar age at study to the cohort studied by Cramer et al. (our 60 years vs Cramer et al.’s 62 years) [31]. A higher proportion of hemorrhagic strokes (25%) was noted in comparison to global proportions of hemorrhagic strokes at 10-15% and this was explained by the Asian stroke population characteristics with higher ICH proportions compared to ischaemic strokes and small sample size [1].In addition, the chronicity of stroke and wide variation in stroke durations were similar characteristics to other populations [19,31]with a median time from stroke of 38 weeks and 25% < 24 weeks.

1.10.2. Summary of safety data

With regards to safety data, this was positive as all 12 (100%) participants completed 30 days of H-Man at home training with 0% dropouts and absence of adverse events anticipated to be related to RAT; such as increased arm pain, shoulder fatigue or spasticity. It is possible that the avoidance of shoulder pain or fatigue was related to proper trunk and arm positioning and provision of passive arm supports and intelligent haptic forces feedback to avoid abnormal biomechanics and excessive arm effort. Notably, no dropouts occurred during the 30-day training phase and a customized post-training satisfaction score recorded >83% (10/12) of overall satisfaction score as quite or very satisfied. Individual participant feedback gave opportunities for further technology and delivery improvements. Similarly, Cramer et al.’s study of 124 patients reported <2% of adverse events in the telerehabilitation group, all unrelated to the study intervention [31].

Regarding the H-Man robotic platform, this was overall robust and reliable, with 2/12 hardware issues, which needed 1 replacement robot each and 2/12 self-limiting software issues; both these issues did not appreciably delay training in the 4 participants.

1.10.3. Summary of objectives

In terms of the primary objectives, these exceeded initial set objectives of >75% compliance. Through performance generated on cloud data, compliance at home was 87.7% with self-directed exergaming, occurring on a mean of >26 /30 days, 6 days per week and a mean duration of 77 minutes daily. This average intensity (~30.3 hours/month) far exceeded the current centre’s standard in-clinic intensity of 8-hourly sessions/month by ~4-fold. Notably, group 2 participants (n=6), who were able to access a total of 8 exergames compared to 3 for group 1, recorded 96.7% (29/30 days) adherence. The level of adherence was comparable to the large study by Cramer et al. (98.3%), where 50% of the delivered telerehabilitation sessions were supervised by professionals [31].

1.11. Telerehabilitation for stroke rehabilitation

Telerehabilitation (TR) for stroke rehabilitation has emerged as a feasible way to deliver various services asynchronously or simultaneously, thus prolonging or intensifying hospital or clinic-based treatments without concomitant strain on healthcare resources. Stroke patients who completed home-based telerehabilitation achieved outcomes equal to or better than those from conventional care during the first 3 months of stroke [29,30].

TR, with or without technology, can potentially overcome the suboptimal intensity of UE practice in the subacute to chronic phases of stroke rehabilitation, whether at home or in the community, thus overcoming various barriers. These include transportation, urban distances, physical barriers, heavy social support if there are mobility challenges contributing to indirect costs; clinic scheduling and inadequate healthcare manpower.

Since the mid-90’s to 2000, in parallel with the internet revolution, there has been a surge of TR to address population-level issues in stroke, such as the reduction of hospitalization stay and costs through a wide range of TR services such as videos, online educational materials or instructions, or supervised training via TR with clinic professionals and RAT used within TR framework. For the latter, several critical factors are needed for successful implementation, such as reliable internet connections for accessing cloud data, secure web-based portals to allow telemonitoring and healthcare professionals access to participant data, and low-cost, non-complex, portable with interactive sensing and gaming devices. Such features are necessary to allow robustness and engagement in the home environment after a brief period of competency training and checks of untrained carers.

TR incorporating repetitive RAT training, which enhances engagement rather than demotivates, automates delivery and progression of exercise intensity progression, adapts performance and assesses performance are also needed [53]. Remote human factors such as prompt attention and encouragement ensure that patients remain supported during TR [54].

1.11.1. Technology considerations for robotics-aided TR at home

Despite the widespread use of RAT in clinics, very few upper limb robotic systems have been developed for use as home-based TR devices. Previous reports of simple distal portable and passive limb robots such as Hand Mentor devices [55] and SCRIPT passive orthosis (SPO) [56]have been described, focusing on usability and participant feedback for further improvements. These devices are passive wrist and hand orthosis, without active generation of forces or control of moments, although undesired torques due to spasticity could be offset. Hence, residual muscle control proximally and within wrist muscles are needed to successfully use SPO, for example. Inbuilt sensors allowed estimations of joint rotations and applied torques to allow interaction with gaming environments.

Guillen-Climent et al., reported a safety and usability study of 9 subacute and chronic stroke patients using an unactuated robotic system with a software system based on interactive gaming, the MERLIN, for daily arm and hand rehabilitation for 3 weeks, including 2 weeks at home with 50% (3x/week for the second week) of the time under supervision [19]. Self-directed movements of the wrist were needed for patients and games included assessment and training content, in total 7 games, including online games and word or puzzle games, challenging both UE motor abilities and cognitive abilities [19]. Moderate observable gains in FMA impairment (total, upper limb and coordination) were observed and high satisfaction scores using objective usability and assistive aid assessment scales, indicating that home-based technology was feasible and safe with motivating responses from participants.

1.11.2. RAT and TR clinical efficacy

In the first clinical study on H-Man utilizing a 2-arm RCT design with combinatory OT, Budhota et al., demonstrated over 24 weeks, FMA gains of Δ4.2 at 19 weeks post-training, after 18 hours over 6 weeks of in-clinic training [14]. Clinical efficacy of these results was similar to lab versions of the H-Man previously used: ΔFMA 4.2 H-Man at clinic [14] vs ΔFMA 3.7 (current Home-based H-Man) at week 24 follow-up with similar arm impairment inclusion criteria, albeit this pilot study did not incorporate COT during the month-long training phase. Overall, these treatment gains in clinic or home are comparable with various RAT in-clinic trials using including end-effector upper limb RAT [15–17,57,58].

Over 24 weeks of follow-up, gains in ΔFMA were sustained over baseline. However, we could not totally ascribe these gains to be due to H-Man training as a concomitant low-intensity standard COT was ongoing in 8/12 (66.7%) participants.

Cramer et al.,demonstrated that the efficacy of upper limb home-based telerehabilitation (TR) to be comparable to in-clinic therapy with non-inferior results, noting that both TR at home and in clinic therapy groups achieved minimal clinically important difference in ΔFMA, exceeding 7 points [31]. In this study, 124 patients were randomized to 2 groups of either TR or clinic-based matched therapy, combining unsupervised and supervised via computer games and dose and duration-matched in-clinic therapy, 70 minutes 6 days per week [31]. In terms of activities of daily living, training is essential to overcoming stroke-related functional disability; the home provides contextual relevance, which is difficult to duplicate in clinics (e.g., opening doorknobs or drawers, using common utensils or fine motor control of everyday objects). Hence, it is important to understand these differences in delivering real-world practice [27]. Our study had lower FMA gains of Δ3.7 compared to Cramer et al., possibly due to a chronic stroke population and lack of combinatory supervised OT in the study protocol.

Home-based self-managed UE therapy can also be regarded as another method for extending UE rehabilitation beyond the clinic. A systematic review by Westlake et al. showed largely equivalent efficacy between clinic and purposely designed, self-managed UE home programmes [28]. Toh et al. performed a meta-analysis on home-based UE rehabilitation on stroke survivors and found new findings that this was more effective in improving hemiplegic upper limb function (SMD 0.28, p < 0.001) than in-clinic conventional therapy. Subgroup analysis revealed that home-based technology provided by electrical stimulation provided more significant improvement in UE function than treatment without the use of technology, possibly due to increased tactile somatosensory and cognitive inputs. (SMD 0.64, p = 0.003) [59].

Comparing home-based robotic-powered assisted interventions with clinic controls, these had modest effects, likely due to the low duration, intensity and limited number of exercises of RAT (3 exercises) compared to COT (34 exercises). Again, this reinforces the importance of RAT design for use in remote locations from close supervision, in promoting adherence through motivational strategies, large numbers of virtual reality varied exercises and affordable cost.

1.11.3. RAT and TR economic evaluation

CEA established RAT at home, using H-Man as a cheap and clinically effective option compared to the alternatives, COT and RAT at clinic. RAT at clinic, compared to COT, incurred higher costs, as the intervention cost included fully OT-supervised sessions and additional robot costs. In concordance with these, a RCT conducted by Fernandez-Garcia et al. showed that a fully supervised mode of RAT at clinic is not a cost-effective option compared to COT [60]. Therefore, RAT at clinic would not represent a preferred mode for therapy delivery in future.

On the other hand, H-Man at home, compared to COT, reduced direct cost by 24%, which was possible by decreasing the need for an OT’s presence during therapy sessions. Lower direct cost and higher clinical gains made H-Man at home more cost-effective than comparators. However, the findings of our CEA differed from the study by Adie et al. [61]. In their analysis, based on a RCT, authors concluded that home-based TR was not a cost-effective option compared to the self-administered Graded Repetitive Arm Supplementary Programme (GRASP). Adie et al.'s unfavourable health-economic results for home-based TR could be attributed to the selection of GRASP - a cost-free comparator (control) and the use of a commercial gaming console as a new intervention, which might not have adequate capabilities for rehabilitation purposes.

Furthermore, our cost outcomes were comparable with previous studies investigating cost of RAT at home [62,63]. Like those studies, our results showed that the major costs were saved through decreased therapist’s presence during therapy provision. However, in our study, (24%) total cost reduction with H-Man at home compared to clinic-based COT was lower than the findings of (65%) Housley S.N. et al. [62] and (44%) Lloréns R.R. et al. [63]. This could be explained by large differences in estimated indirect costs, such as transportation cost. In the case of Singapore, transportation costs were minimal and had a negligible effect on overall cost savings with H-Man at home; While other studies reported 75% [62] - 88% [63] of total cost reduction due to decreased transportation during RAT at home.

In Singapore, over a five-year span, if by the end of the fifth year, 80% of stroke survivors use RAT at home, it could potentially yield annual cost savings of 19%. However, it is essential to note that the actual cost savings of a novel approach would be contingent upon the number of individuals referred to RAT at home. Thus, uptake rates would require further validation and scrutiny by the stakeholders.

1.12. Study limitations

We highlight the following limitations: the small sample size from a single site which may limit generalizability and under representation of older strokes. A small sample size did not provide enough statistical power to detect differences in economic outcomes. Objective evaluation of system usability and motivational scales were not used. Also, retrospectively collected cost data for RAT at clinic and COT did not represent actual incurred cost; rather, it was estimated based on current attended sessions and estimations were also calculated for transportation and waiting time.

Data on health outcome measures (SSQOL) for RAT at clinic and COT groups were not available to estimate health utilities. Hence, we performed CEA using clinical outcomes (FMA).

To our knowledge, this is the first local feasibility trial of a home-based actuated end-effector robot with telemonitoring demonstrating modest sustained gains in arm motor impairment and function with a favourable cost-effectiveness analysis. High satisfaction ratings by participants and subjective feedback provided suggestions for further technology improvements, such as a smaller robotic footprint making it easier to accommodate in home settings. Further comparative RCTs with larger sample sizes are needed to evaluate these outcomes and compare them with usual treatment. Cost-utility studies, willingness to pay and adoption potentials also need to be evaluated.

Ethics approval and consent to participate

Institutional review board approvals were obtained from National Healthcare Group (NHG 2021/00156).

All participants gave written informed consent.

Consent for publication

Participant gave written consent for image in Fig. 1b.

Availability of data and materials

These data sets (anonymised) will be made available on request.

Competing interests

CKSG and KCWK both declare interests that they were not involved in participant screening, informed consent, participant recruitment, data analysis nor data safety monitoring due to their co-sharing royalties from H-Man co-development.

AH declares interest as co-founder and CEO of the company leading the development of H-Man and was not involved in the concept, conduct of trial, data monitoring or analysis.

The following authors have no competing interests: OGA, OPL, KLW, GKH, NCY, PTK, CWB, ST, MM, KS, HJC.

Funding

This work was supported by Temasek Foundation (TF REF: TF1920-ATC-01) and the National Health Innovation Centre, Singapore (NHIC) Innovation to Industry (I2I) Grant (NHIC-I2I-2104007). Grant support durations were from 2021-2023.

Author contributions

i. Ethical approval CKSG, CWB, LJAM, OPL

ii. Device provision, technical support, cloud data and monitoring: OGA, HA, MM

iii. Study concept, design: CKSG, KCWK, OPL, KLW, GKH

iv. Clinical trial screening, recruitment, tele monitoring: OPL, NCY, PTK, CWB, LJAM

v. Participant safety monitoring: OPL, NCY, PTK, CWB, LJAM

vi. Data analysis, clean up, tables, figures: KLW, GKH, CWB, ST, MM, KS

vii. Health Economics Analysis: ST, KLW, GKH, HJC, KS

viii. Manuscript Preparation: OGA, CKSG, KLW, GKH, ST

Equal contribution: OGA, CKSG, ST, KLW, GKH

All authors have reviewed and agreed on the final version of the manuscript.

Acknowledgements

Our patients and their caregivers for their participation and support.

Author details

Ollinger GA: 0000-0003-3834-9171

Chua KSG: 0000-0001-9809-3425

Ong PL: 0000-0002-0910-6683

Kuah CWK: 0000-0001-6070-3187

Plunkett TK: 0009-0007-4596-1525

Ng CY: 0009-0008-9923-7679

Khin LW: 0000-0001-9181-0372

Goh KH: 0000-0001-7327-0106

Chong WB: 0009-0002-1888-8813

Low JAM: 0009-0000-8241-6269

Mushtaq M: 0000-0001-5683-0757

Samkharadze T: 0000-0003-2969-4555

Kager S: 0000-0002-1160-1501

Cheng H-J: 0000-0002-6203-4543

Hussain A: 0009-0006-3991-301X

1. 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;20:795–820.
Jørgensen HS, Reith J, Nakayama H, Kammersgaard LP, Raaschou HO, Olsen TS. What Determines Good Recovery in Patients With the Most Severe Strokes?: The Copenhagen Stroke Study. Stroke [Internet]. 1999 [cited 2024 Jun 26];30:2008–12. Available from: https://www.ahajournals.org/doi/10.1161/01.STR.30.10.2008
Rathore SS, Hinn AR, Cooper LS, Tyroler HA, Rosamond WD. Characterization of Incident Stroke Signs and Symptoms: Findings From the Atherosclerosis Risk in Communities Study. Stroke [Internet]. 2002 [cited 2024 Jun 26];33:2718–21. Available from: https://www.ahajournals.org/doi/10.1161/01.STR.0000035286.87503.31
Nakayama H, Stig Jørgensen H, Otto Raaschou H, Skyhøj Olsen T. Recovery of upper extremity function in stroke patients: The Copenhagen stroke study. Arch Phys Med Rehabil [Internet]. 1994 [cited 2024 Jun 26];75:394–8. Available from: https://linkinghub.elsevier.com/retrieve/pii/0003999394901619
Lawrence ES, Coshall C, Dundas R, Stewart J, Rudd AG, Howard R, et al. Estimates of the Prevalence of Acute Stroke Impairments and Disability in a Multiethnic Population. Stroke [Internet]. 2001 [cited 2024 Jun 27];32:1279–84. Available from: https://www.ahajournals.org/doi/10.1161/01.STR.32.6.1279
Dean SG, Poltawski L, Forster A, Taylor RS, Spencer A, James M, et al. Community-based Rehabilitation Training after stroke: protocol of a pilot randomised controlled trial (ReTrain). BMJ Open [Internet]. 2016 [cited 2024 Jun 26];6:e012375. Available from: https://bmjopen.bmj.com/lookup/doi/10.1136/bmjopen-2016-012375
Venketasubramanian N, Chen CLH. Burden of Stroke in Singapore. Int J Stroke [Internet]. 2008 [cited 2022 Feb 8];3:51–4. Available from: http://journals.sagepub.com/doi/10.1111/j.1747-4949.2008.00181.x
Koh GC-H, Yen SC, Tay A, Cheong A, Ng YS, De Silva DA, et al. Singapore Tele-technology Aided Rehabilitation in Stroke (STARS) trial: protocol of a randomized clinical trial on tele-rehabilitation for stroke patients. BMC Neurol. 2015;15:161.
Kleim J, Jones T. Principles of Experience-Dependent Neural Plasticity: Implications for Rehabilitation After Brain Damage. J Speech Lang Hear Res. 2008;51:S225–39.
Miller EL, Murray L, Richards L, Zorowitz RD, Bakas T, Clark P, et al. Comprehensive Overview of Nursing and Interdisciplinary Rehabilitation Care of the Stroke Patient. Stroke. 2010;41:2402–48.
Aisen ML, Krebs HI, Hogan N, McDowell F, Volpe BT. The Effect of Robot-Assisted Therapy and Rehabilitative Training on Motor Recovery Following Stroke. Arch Neurol. 1997;54:443–6.
Krebs HI, Mernoff S, Fasoli SE, Hughes R, Stein J, Hogan N. A comparison of functional and impairment-based robotic training in severe to moderate chronic stroke: a pilot study. NeuroRehabilitation. 2008;23:81–7.
Volpe BT, Lynch D, Rykman-Berland A, Ferraro M, Galgano M, Hogan N, et al. Intensive Sensorimotor Arm Training Mediated by Therapist or Robot Improves Hemiparesis in Patients With Chronic Stroke. Neurorehabil Neural Repair [Internet]. 2008 [cited 2024 Jun 26];22:305–10. Available from: http://journals.sagepub.com/doi/10.1177/1545968307311102
Budhota A, Chua KSG, Hussain A, Kager S, Cherpin A, Contu S, et al. Robotic Assisted Upper Limb Training Post Stroke: A Randomized Control Trial Using Combinatory Approach Toward Reducing Workforce Demands. Front Neurol [Internet]. 2021 [cited 2022 Jun 7];12:622014. Available from: https://www.frontiersin.org/articles/10.3389/fneur.2021.622014/full
Lo AC, Guarino PD, Richards LG, Haselkorn JK, Wittenberg GF, Federman DG, et al. Robot-assisted therapy for long-term upper-limb impairment after stroke. N Engl J Med. 2010;362:1772–83.
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 [Internet]. 2017 [cited 2024 Jun 26];31:107–21. Available from: http://journals.sagepub.com/doi/10.1177/1545968316666957
Rodgers H, Bosomworth H, Krebs HI, van Wijck F, Howel D, Wilson N, et al. Robot assisted training for the upper limb after stroke (RATULS): a multicentre randomised controlled trial. The Lancet [Internet]. 2019;394:51–62. Available from: https://www.embase.com/search/results?subaction=viewrecord&id=L2002217972&from=export
Krebs HI, Hogan N, Aisen ML, Volpe BT. Robot-aided neurorehabilitation. IEEE Trans Rehabil Eng [Internet]. 1998 [cited 2024 Jun 26];6:75–87. Available from: http://ieeexplore.ieee.org/document/662623/
Guillén-Climent S, Garzo A, Muñoz-Alcaraz MN, Casado-Adam P, Arcas-Ruiz-Ruano J, Mejías-Ruiz M, et al. A usability study in patients with stroke using MERLIN, a robotic system based on serious games for upper limb rehabilitation in the home setting. J NeuroEngineering Rehabil [Internet]. 2021 [cited 2022 Mar 20];18:41. Available from: https://jneuroengrehab.biomedcentral.com/articles/10.1186/s12984-021-00837-z
Hussain A, Budhota A, Hughes CML, Dailey WD, Vishwanath DA, Kuah CWK, et al. Self-Paced Reaching after Stroke: A Quantitative Assessment of Longitudinal and Directional Sensitivity Using the H-Man Planar Robot for Upper Limb Neurorehabilitation. Front Neurosci [Internet]. 2016 [cited 2024 Jun 26];10. Available from: http://journal.frontiersin.org/article/10.3389/fnins.2016.00477/full
Kager S, Hussain A, Budhota A, Dailey WD, Hughes CM, Deshmukh VA, et al. Work with me, not for me: Relationship between robotic assistance and performance in subacute and chronic stroke patients. J Rehabil Assist Technol Eng [Internet]. 2019 [cited 2024 Jun 26];6:205566831988158. Available from: http://journals.sagepub.com/doi/10.1177/2055668319881583
Lambercy O, Lehner R, Chua K, Wee SK, Rajeswaran DK, Kuah CWK, et al. Neurorehabilitation From a Distance: Can Intelligent Technology Support Decentralized Access to Quality Therapy? Front Robot AI [Internet]. 2021 [cited 2023 May 30];8:612415. Available from: https://www.frontiersin.org/articles/10.3389/frobt.2021.612415/full
Teasell R, Mehta S, Pereira S, McIntyre A, Janzen S, Allen L, et al. Time to Rethink Long-Term Rehabilitation Management of Stroke Patients. Top Stroke Rehabil [Internet]. 2012 [cited 2024 Jun 26];19:457–62. Available from: http://www.tandfonline.com/doi/full/10.1310/tsr1906-457
Ward NS, Brander F, Kelly K. Intensive upper limb neurorehabilitation in chronic stroke: outcomes from the Queen Square programme. J Neurol Neurosurg Psychiatry [Internet]. 2019 [cited 2024 Jun 26];90:498–506. Available from: https://jnnp.bmj.com/lookup/doi/10.1136/jnnp-2018-319954
Fugl-Meyer AR, Jääskö L, Leyman I, Olsson S, Steglind S. The post-stroke hemiplegic patient. 1. a method for evaluation of physical performance. Scandinavian journal of rehabilitation medicine. 1975;7:13–31.
Chen AW, Koh YT, Leong SW, Ng LW, Lee PS, Koh GC. Post Community Hospital Discharge Rehabilitation Attendance: Self-Perceived Barriers and Participation Over Time. Ann Acad Med Singapore. 2014;43:136–44.
Van Vliet P, Tavener M, Van Wijck FM, Alt Murphy M. Editorial: Home-based training to reduce upper limb functional impairment post-stroke. Front Neurol [Internet]. 2023 [cited 2024 Jun 26];14:1309954. Available from: https://www.frontiersin.org/articles/10.3389/fneur.2023.1309954/full
Westlake K, Akinlosotu R, Udo J, Goldstein Shipper A, Waller SM, Whitall J. Some home-based self-managed rehabilitation interventions can improve arm activity after stroke: A systematic review and narrative synthesis. Front Neurol [Internet]. 2023 [cited 2024 Jun 26];14:1035256. Available from: https://www.frontiersin.org/articles/10.3389/fneur.2023.1035256/full
Asano M, Tai BC, Yeo FY, Yen SC, Tay A, Ng YS, et al. Home-based tele-rehabilitation presents comparable positive impact on self-reported functional outcomes as usual care: The Singapore Tele-technology Aided Rehabilitation in Stroke (STARS) randomised controlled trial. J Telemed Telecare [Internet]. 2021 [cited 2024 Jun 26];27:231–8. Available from: http://journals.sagepub.com/doi/10.1177/1357633X19868905
Sarfo FS, Adusei N, Ampofo M, Kpeme FK, Ovbiagele B. Pilot trial of a tele-rehab intervention to improve outcomes after stroke in Ghana: A feasibility and user satisfaction study. J Neurol Sci. 2018;387:94–7.
Cramer SC, Dodakian L, Le V, See J, Augsburger R, McKenzie A, et al. Efficacy of Home-Based Telerehabilitation vs In-Clinic Therapy for Adults After Stroke: A Randomized Clinical Trial. JAMA Neurol [Internet]. 2019 [cited 2022 Mar 20];76:1079. Available from: https://jamanetwork.com/journals/jamaneurology/fullarticle/2736341
Articares Pte Ltd. H-Man [Internet]. https://articares.com. 2023 [cited 2024 Jun 27]. Available from: https://articares.com/h-man/
Clinic to Home Robotics-assisted Telerehabilitation for Upper Limb (CHISEL) [Internet]. ClinicalTrials.gov. 2022 [cited 2024 Jun 28]. Available from: https://clinicaltrials.gov/study/NCT05212181
Campolo D, Tommasino P, Gamage K, Klein J, Hughes CML, Masia L. H-Man: A planar, H-shape Cabled Differential Robotic Manipulandum for Experiments on Human Motor Control. J Neurosci Methods [Internet]. 2014 [cited 2022 Jun 13];235:285–97. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0165027014002477
Articares Pte Ltd. CARE Platform [Internet]. https://articares.com/care_platform/. 2023 [cited 2024 Jun 27]. Available from: https://articares.com/care_platform/
Ministry of Health Singapore. HealthTech Instruction Manual [Internet]. Heal. Instr. Man. 2010 [cited 2024 Jun 25]. Available from: https://www.moh.gov.sg/hpp/all-healthcare-professionals/HIM
Nasreddine ZS, Phillips NA, Bédirian V, Charbonneau S, Whitehead V, Collin I, et al. The Montreal Cognitive Assessment, MoCA: A Brief Screening Tool For Mild Cognitive Impairment. J Am Geriatr Soc [Internet]. 2005 [cited 2024 Jun 27];53:695–9. Available from: https://agsjournals.onlinelibrary.wiley.com/doi/10.1111/j.1532-5415.2005.53221.x
Lee C-Y, Howe T-H. Effectiveness of Activity-Based Task-Oriented Training on Upper Extremity Recovery for Adults With Stroke: A Systematic Review. Am J Occup Ther [Internet]. 2024 [cited 2024 Jun 26];78:7802180070. Available from: https://doi.org/10.5014/ajot.2024.050391
Pathak A, Gyanpuri V, Dev P, Dhiman N. The Bobath Concept (NDT) as rehabilitation in stroke patients: A systematic review. J Fam Med Prim Care [Internet]. 2021 [cited 2024 Jun 26];10:3983. Available from: https://journals.lww.com/jfmpc/Fulltext/2021/11000/The_Bobath_Concept__NDT__as_rehabilitation_in.8.aspx
Likert R. A technique for measurement of attitudes. Archives of Psychology. 1932;140:5–55.
Yozbatiran N, Der-Yeghiaian L, Cramer SC. A Standardized Approach to Performing the Action Research Arm Test. Neurorehabil Neural Repair [Internet]. 2008 [cited 2024 Jun 26];22:78–90. Available from: http://journals.sagepub.com/doi/10.1177/1545968307305353
Van Der Lee JH, De Groot V, Beckerman H, Wagenaar RC, Lankhorst GJ, Bouter LM. The intra- and interrater reliability of the action research arm test: A practical test of upper extremity function in patients with stroke. Arch Phys Med Rehabil [Internet]. 2001 [cited 2024 Jun 27];82:14–9. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0003999301513015
Jamar Hydraulic Hand Dynamometer (5030J1) [Internet]. JLWFORCE.COM. 1984 [cited 2024 Jun 26]. Available from: https://cdn.shopify.com/s/files/1/2272/3691/t/312/assets/421dded0a8f4--Jamar-Instruction-Manual.pdf?v=1654183977
Williams LS, Weinberger M, Harris LE, Clark DO, Biller J. Development of a Stroke-Specific Quality of Life Scale. Stroke [Internet]. 1999 [cited 2024 Jun 26];30:1362–9. Available from: https://www.ahajournals.org/doi/10.1161/01.STR.30.7.1362
Bohannon RW, Smith MB. Interrater reliability of a modified ashworth scale of muscle spasticity. Phys Ther. 1987;206–7.
Hawker GA, Mian S, Kendzerska T, French M. Measures of adult pain: Visual Analog Scale for Pain (VAS Pain), Numeric Rating Scale for Pain (NRS Pain), McGill Pain Questionnaire (MPQ), Short‐Form McGill Pain Questionnaire (SF‐MPQ), Chronic Pain Grade Scale (CPGS), Short Form‐36 Bodily Pain Scale (SF‐36 BPS), and Measure of Intermittent and Constant Osteoarthritis Pain (ICOAP). Arthritis Care Res [Internet]. 2011 [cited 2024 Jun 27];63. Available from: https://acrjournals.onlinelibrary.wiley.com/doi/10.1002/acr.20543
Isaac S, Michael WB. Handbook in research and evaluation [Internet]. San Diego, CA: Educational and Industrial Testing Services; 1995. Available from: https://www.ojp.gov/ncjrs/virtual-library/abstracts/handbook-research-and-evaluation-collection-principles-methods-and
Turner HC, Archer RA, Downey LE, Isaranuwatchai W, Chalkidou K, Jit M, et al. An Introduction to the Main Types of Economic Evaluations Used for Informing Priority Setting and Resource Allocation in Healthcare: Key Features, Uses, and Limitations. Front Public Health [Internet]. 2021 [cited 2023 Jun 6];9:722927. Available from: https://www.frontiersin.org/articles/10.3389/fpubh.2021.722927/full
Sullivan SD, Mauskopf JA, Augustovski F, Jaime Caro J, Lee KM, Minchin M, et al. Budget Impact Analysis—Principles of Good Practice: Report of the ISPOR 2012 Budget Impact Analysis Good Practice II Task Force. Value Health [Internet]. 2014 [cited 2023 Oct 27];17:5–14. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1098301513042356
Singapore Stroke Registry Annual Report 2021 [Internet]. Singapore, Singapore: National Registry of Diseases Office; 2023 p. 1–53. Available from: https://www.nrdo.gov.sg/docs/librariesprovider3/default-document-library/ssr-annual-report-2019.pdf?sfvrsn=594314c4_0
Ministry of Health Singapore. Stroke Care Indicators in Singapore. Unpublished; 2013.
Sun Y, Lee SH, Heng BH, Chin VS. 5-year survival and rehospitalization due to stroke recurrence among patients with hemorrhagic or ischemic strokes in Singapore. BMC Neurol [Internet]. 2013 [cited 2023 Oct 5];13:133. Available from: http://bmcneurol.biomedcentral.com/articles/10.1186/1471-2377-13-133
Frolov AA, Kozlovskaya IB, Biryukova EV, Bobrov PD. Use of Robotic Devices in Post-Stroke Rehabilitation. Neurosci Behav Physiol [Internet]. 2018 [cited 2024 Jun 26];48:1053–66. Available from: https://link.springer.com/10.1007/s11055-018-0668-3
Manjunatha H, Pareek S, Jujjavarapu SS, Ghobadi M, Kesavadas T, Esfahani ET. Upper Limb Home-Based Robotic Rehabilitation During COVID-19 Outbreak. Front Robot AI [Internet]. 2021 [cited 2024 Jun 26];8:612834. Available from: https://www.frontiersin.org/articles/10.3389/frobt.2021.612834/full
Habilis Europe: a new concept for telerehabilitation [Internet]. 2020 [cited 2024 Jun 27]. Available from: https://www.habiliseurope.com
Ates S, Lobo-Prat J, Lammertse P, van der Kooij H, Stienen AHA. SCRIPT Passive Orthosis: Design and technical evaluation of the wrist and hand orthosis for rehabilitation training at home. 2013 IEEE 13th Int Conf Rehabil Robot ICORR [Internet]. Seattle, WA: IEEE; 2013 [cited 2023 Jan 17]. p. 1–6. Available from: http://ieeexplore.ieee.org/document/6650401/
Klamroth-Marganska V, Blanco J, Campen K, Curt A, Dietz V, Ettlin T, et al. Three-dimensional, task-specific robot therapy of the arm after stroke: a multicentre, parallel-group randomised trial. Lancet Neurol [Internet]. 2014 [cited 2024 Jun 27];13:159–66. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1474442213703053
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 [Internet]. 2018; Available from: http://dx.doi.org/10.1002/14651858.CD006876.pub5
Toh SFM, Chia PF, Fong KNK. Effectiveness of home-based upper limb rehabilitation in stroke survivors: A systematic review and meta-analysis. Front Neurol [Internet]. 2022 [cited 2024 Jun 26];13:964196. Available from: https://www.frontiersin.org/articles/10.3389/fneur.2022.964196/full
Fernandez-Garcia C, Ternent L, Homer TM, Rodgers H, Bosomworth H, Shaw L, et al. Economic evaluation of robot-assisted training versus an enhanced upper limb therapy programme or usual care for patients with moderate or severe upper limb functional limitation due to stroke: Results from the RATULS randomised controlled trial. BMJ Open [Internet]. 2021;11. Available from: https://www.embase.com/search/results?subaction=viewrecord&id=L635124102&from=export
Adie K, Schofield C, Berrow M, Wingham J, Humfryes J, Pritchard C, et al. Does the use of Nintendo Wii Sports(TM) improve arm function? Trial of Wii(TM) in Stroke: a randomized controlled trial and economics analysis. Clin Rehabil. 2017;31:173–85.
Housley S, Garlow A, Ducote K, Howard A, Thomas T, Wu D, et al. Increasing Access to Cost Effective Home-Based Rehabilitation for Rural Veteran Stroke Survivors. Austin J Cerebrovasc Stroke. 2016;3(2):1–11.
Lloréns R, Noé E, Colomer C, Alcañiz M. Effectiveness, Usability, and Cost-Benefit of a Virtual Reality–Based Telerehabilitation Program for Balance Recovery After Stroke: A Randomized Controlled Trial. Arch Phys Med Rehabil [Internet]. 2015 [cited 2022 Feb 8];96:418-425.e2. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0003999314012209
Balasubramanian S, Melendez-Calderon A, Burdet E. A Robust and Sensitive Metric for Quantifying Movement Smoothness. IEEE Trans Biomed Eng [Internet]. 2012 [cited 2024 Jun 27];59:2126–36. Available from: http://ieeexplore.ieee.org/document/6104119/
Capio CM, Poolton JM, Sit CHP, Eguia KF, Masters RSW. Reduction of errors during practice facilitates fundamental movement skill learning in children with intellectual disabilities. J Intellect Disabil Res [Internet]. 2013 [cited 2024 Jun 27];57:295–305. Available from: https://onlinelibrary.wiley.com/doi/10.1111/j.1365-2788.2012.01535.x
Land Transport Authority Singapore. Transport Fare Calculator [Internet]. 2023. Available from: https://www.lta.gov.sg/content/ltagov/en/map/fare-calculator.html
Taxi-Calculator.com. Taxi Fare Calculator for Singapore [Internet]. 2023. Available from: https://www.taxi-calculator.com/taxi-fare-singapore/440
Anglo Caregivers. Maid vs Caregiver - 4 Differences, 4 Considerations [Internet]. Anglo Caregiv. 2022 [cited 2023 Aug 11]. Available from: https://www.anglocaregivers.com/guides/maid-vs-caregiver-4-key-differences-and-4-considerations#salary
Ministry of Manpower Singapore. Gross Monthly Income [Internet]. Singapore: Ministry of Manpower Singapore. Manpower Research and Statistics Department; 2023 May. Available from: https://stats.mom.gov.sg/Pages/Income-Summary-Table.aspx
Yeoh BSA, Huang S, Iii JG. Migrant Female Domestic Workers: Debating the Economic, Social and Political Impacts in Singapore. Int Migr Rev [Internet]. 1999 [cited 2023 Nov 8];33:114. Available from: https://www.jstor.org/stable/2547324?origin=crossref
Housing and Development Board Singapore. Market Rental Rates of Flatss by Housing and Development Board Singapore [Internet]. 2023. Available from: https://services2.hdb.gov.sg/webapp/BR12AWRentalEnq/BR12PSearch.jsp

Competing interest reported. CKSG and KCWK both declare interests that they were not involved in participant screening, informed consent, participant recruitment, data analysis nor data safety monitoring due to their co-sharing royalties from H-Man co-development. AH declares interest as co-founder and CEO of the company leading the development of H-Man and was not involved in the concept, conduct of trial, data monitoring or analysis. The following authors have no competing interests: OGA, OPL, KLW, GKH, NCY, PTK, CWB, ST, MM, KS, HJC.

Appendix.docx

Download PDF

Editorial decision: Revision requested
19 Aug, 2024
Reviews received at journal
18 Aug, 2024
Reviews received at journal
16 Aug, 2024
Reviews received at journal
16 Aug, 2024
Reviews received at journal
11 Aug, 2024
Reviewers agreed at journal
08 Aug, 2024
Reviewers agreed at journal
06 Aug, 2024
Reviewers agreed at journal
06 Aug, 2024
Reviewers agreed at journal
06 Aug, 2024
Reviewers agreed at journal
05 Aug, 2024
Reviews received at journal
31 Jul, 2024
Reviewers agreed at journal
19 Jul, 2024
Reviewers invited by journal
19 Jul, 2024
Editor assigned by journal
08 Jul, 2024
Submission checks completed at journal
08 Jul, 2024
First submitted to journal
05 Jul, 2024

You are reading this latest preprint version

Telerehabilitation using a 2-D planar arm rehabilitation robot for hemiparetic stroke: A feasibility study of clinic-to-home exergaming therapy

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials and Methods

2.1. Overview of methods

2.2. Hardware

2.3. Robotic intervention (exergames)

2.4. Remote monitoring software

2.5. Study setting

2.6. Study participants

2.7. Study protocol overview

2.7.1. Description of in-clinic phase

2.7.2. Home training phase

2.7.3. Follow-up phase

2.8. Outcome measures

2.8.1. Therapy plan: compliance and safety data

2.8.2. Participant subjective ratings

2.8.3. Standardised clinical outcomes

2.8.4. Statistical analysis

2.8.5. Cost-effectiveness analysis (CEA)

2.8.6. Budget impact analysis (BIA)

3. Results

3.1. Participants and study

3.1.1. Follow-up phase

3.2. Training compliance obtained from web-based platform data

3.2.1. Training time and compliance with the training plan and training patterns

3.3. Clinical outcomes summary

3.4. Health-economic outcomes summary

4. Discussion

4.1. General summary of study protocol, target population, safety and usability data

1.10.1. Sample baseline characteristics

1.10.2. Summary of safety data

1.10.3. Summary of objectives

1.11. Telerehabilitation for stroke rehabilitation

1.11.1. Technology considerations for robotics-aided TR at home

1.11.2. RAT and TR clinical efficacy

1.11.3. RAT and TR economic evaluation

1.12. Study limitations

Conclusions

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1