Sage Journals: Discover world-class research

Abstract

Objective

To identify, map and synthesize the extent and nature of existing studies on the use of home-based digital technology to support post-stroke upper limb rehabilitation.

Data sources

A comprehensive literature search was completed between 30 May 2022 and 05 April 2023, from seven online databases (CINAHL, Cochrane Library, PubMed, ScienceDirect, IEEExplore, Web of Science and PEDro), Google Scholar and the reference lists of already identified articles.

Methods

A scoping review was conducted according to Arksey and O’Malley (2005), and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. All English-language studies reporting on the use of home-based digital technology to support upper limb post-stroke rehabilitation were eligible for inclusion.

Results

The search generated a total of 1895 records, of which 76 articles met the inclusion criteria. Of these, 52 were experimental studies and the rest, qualitative, case series and case studies. Of the overall 2149 participants, 2028 were stroke survivors with upper limb impairment. The majority of studies were aimed at developing, designing and/or assessing the feasibility, acceptability and efficacy of a digital system for poststroke upper limb rehabilitation in home settings. The thematic analysis found six major categories: Tele-rehabilitation (n = 29), games (n = 45), virtual reality (n = 26), sensor (n = 22), mobile technology (n = 22), and robotics (n = 8).

Conclusion

The digital technologies used in post-stroke upper limb rehabilitation were multimodal, and system-based comprising telerehabilitation, gamification, virtual reality, mobile technology, sensors and robotics. Furthermore, future research should focus to determine the effectiveness of these modalities.

Keywords

Poststroke rehabilitation upper limb rehabilitation home-based technology digital health

Introduction

Poststroke rehabilitation is essential for stroke survivors to optimize and improve their function and enhance their quality of life, with the overall aim of achieving maximum independence in their everyday activities.¹ Over the past two decades, significant progress has been made in the development of interventions for stroke rehabilitation.² However, the global need for rehabilitation services is largely unmet, and services have been severely disrupted by the coronavirus disease 2019 (COVID-19) pandemic. As a result, today, more than half of stoke survivors in some low- and middle-income nations do not receive the rehabilitation service they require.³ On the other hand, the current COVID-19 pandemic has caused a dramatic technological transformation in the way that a post-stroke rehabilitation service could be delivered to stroke survivors within their own homes.⁴

One of the long-term impairments after stroke is upper limb weakness. According to the study by Meyer et al.⁵ the prevalence of upper-limb somatosensory impairment after stroke was estimated between 21% and 54%. Evidences indicates that the proportion of recovery of initial motor impairment in patients with a functional corticospinal tract made up to 63% (95% CI, 55%–70%).⁶

Traditionally, upper limb rehabilitation has been carried out in rehabilitation centres or hospitals, but due to the increasing demand for home-based rehabilitation, the use of digital technology has become a more accessible solution for patients. Thus, the recent advancements in digital and telecommunication technologies have created an unprecedented opportunity for post-stroke rehabilitation to adapt to new approaches to care using digital technology innovations. These digital technologies include telemedicine, artificial intelligence, machine learning, deep learning, fifth generation telecommunication networks, the Internet of Things, home monitoring devices, augmented and virtual reality (VR).⁷

Despite this, digital exclusion has also been reported as a challenge for health care digital transformation. For example, a recent report showed that 6% of households in the UK had no access to the internet by the end of 2021. This could be tackled by supporting users to get online and providing essential digital skills for less confident users.⁸

Nevertheless, the up-to-date evidence base about the use of digital technology for post-stroke upper limb rehabilitation in home settings is not well investigated and mapped. Therefore, the aim of this scoping review was to identify, map and synthesize the extent and nature of research activity on the use of home-based digital technology to support post-stroke upper limb rehabilitation.

Methods

A scoping review was conducted according to the Arksey and O’Malley,⁹ which includes five sequential steps; (a) defining a research question, (b) identifying relevant studies, (c) selecting related studies (d) charting data, and (d) collating, summarizing and reporting the result. The reporting of our scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist.¹⁰ Accordingly, the primary review question was ‘What digital technology is used to support poststroke upper limb rehabilitation in home settings?’.

Prior to identifying relevant literature and sources, two authors (GG, APA) agreed the key words to use during the search for articles and databases bases to be accessed. The search strategy for electronic databases was derived from the research question and key concept definitions. The electronic database search was performed from 30 May 2022 to 05 April 05 2023 in CINAHL, Cochrane Library, PubMed, ScienceDirect, IEEExplore, Web of Science, PEDro, and Google Scholar as relevant search engine for grey literature. In addition, articles were included through a review of the reference lists of already identified articles. The main terms used in database searches were ‘home, digital Technology, Tele medicine, Tele health, eHealth, mHealth, Tele rehabilitation, upper limb rehabilitation, upper limb, stroke, rehabilitation’ (Full search strategies can be found in Supplemental Table 1).

The process of article selection was assisted with Mendeley reference manager software version 1.19.4. After importing all search results into a single folder, duplicate search results were identified and merged. Two authors (GG, APA) scanned the titles and abstracts of all articles reporting on the use of home-based digital technology in upper limb post-stroke rehabilitation. Full-text articles were thoroughly read by the lead author to determine eligibility, and the decision whether to include an article was based on the consensus of two authors (GG, APA) (Figure 1). As per the Arksey and O’Malley publication, the selection of studies for review was based on their pertinence to the review question rather than their level of methodological rigour.⁹ Articles were reviewed for eligibility based on the following inclusion and exclusion criteria;

Figure 1.

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram shows article selection process.

Inclusion criteria

Articles published in English between 1 January 2010 and 05 April 2023.

Study population of individuals with any degree of upper limb weakness after stroke.

Studies conducted in home settings.

Studies conducted with any type of digital intervention to support upper limb rehabilitation.

Exclusion criteria

Editorials, registers, opinion pieces, letters and conference papers.

Articles with no separate report for the eligible study population in studies with mixed participants.

Next, a data-charting excel sheet was prepared to be able to capture relevant information on key study patterns and characteristics. Authors discussed and agreed on the content of the charting for and the following information was included: Author(s), year of publication, study location (country); digital technologies used/interventions involved, and duration (if any), study sample, study design, outcome measures and results relevant to the research question of this scoping review (Supplemental Table 2). The first and last authors tested the charting sheet using three articles, who also actually extracted data from all included articles. Any disagreements between the two authors were resolved by the middle author.

An inductive thematic approach was adapted for analysis and synthesis of results.¹¹ After a thorough reading of the final selected articles and fully understanding of patterns, and we identified certain characteristics and concepts out of the extensive and widely dispersed papers conducted on the use of digital technologies for poststroke upper limb rehabilitation in home settings. Next, codes and themes were created using NVIVO. Furthermore, we mapped, summarized and presented the articles according to their aims, type of digital interventions, methods and main outcomes using texts, table and chart. All authors agreed on the findings, final thematic groups, and ways of result presentation. The trustworthiness of findings was enhanced by the research, clinical practice, and academic experience of the authors in the field.

Results

The search generated a total of 1895 records (1848 from online databases and 47 from other sources), of which 487 were removed for duplicates. Of the remaining, 1408 were excluded after title and/or abstract screening, leaving 113 articles for full-text review. Thirty-seven full articles were determined to be irrelevant in reporting the use of home-based digital technology for upper limb rehabilitation of stroke patients, leaving 76 articles^12–87 selected for inclusion in the review (Figure 1).

Overall, 76 articles met the inclusion criteria. Out of these, the majority were conducted in the USA (n = 31) and Europe (n = 28); 52 were experimental studies, and the rest, mixed, qualitative, case studies. The sample sizes in the studies ranged from 1 to 240, with a total of 2149 participants. Of these, 2028 participants were stroke survivors with upper limb impairment while the rest were non-stroke (control group) service users (Supplemental Table 2).

The papers mainly aimed to investigate the development or design (n = 14) feasibility (n = 21), usability (n = 12), efficacy (n = 8), acceptability (n = 7) and user adherence (n = 5), of home-based digital technologies for upper limb rehabilitation in stroke survivors. Furthermore, the thematic analysis identified six thematic areas of digital interventions for poststroke upper limb rehabilitation in home settings: Tele-rehabilitation, games, VR, sensor, mobile technology, and robotics (Table 1).

Table 1.

The number of studies is based on their aims and thematic areas.

	Number of articles	References
Aims
Feasibility	21	^{13,15,16,19,28,30,32,35,41,42,45,48,50,51,64,72,81,84,86,87,91}
Design/development	14	^{18,20,28,29,41,43,46,53,54,61,67,69,80,89}
Usability	12	^{14,15,19,24,25,37,40,55,73,74,78,82}
Efficacy	8	^{13,23,28,61,65–67,84}
Acceptability	7	^{59,61–64,86,91}
Adherence	5	^{28,58–60,86}
Thematic areas
Games	45	^{15,17–21,23,26–29,33,35,36,40,42,43,45,46,49,52–56,61–66,68,69,71,72,74,77–79,82–85,89,91}
Telerehabilitation	29	^{19,21,23,24,26–28,30,35–37,41,44,48,50,53,55,59,64,65,69,71,72,74,79,81,83,86,89}
Virtual/mixed reality	26	^{12,15,18,20,28,29,33,35,36,38,40,42,45–47,51,56,58,69,72,78,79,82,84,89,91}
Sensor	22	^{15,16,18,21,22,26,31,34,39,45,46,50,51,54,57,58,61,78,82,84,87,89}
Mobile technology	22	^{12,14,20,21,24,25,30,34,37,48,50,51,60,61,67,73–75,80,86,91,94}
Robotics	8	^{27,41,49,55,69,79,83,85}

Themes

Tele-rehabilitation

Telerehabilitation can be defined as the delivery of rehabilitation services via various technologies remotely.⁸⁸ A total of 29 articles^{19,21,23,24,26–28,30,35–37,41,44,48,50,53,55,59,64,65,69,71,72,74,79,81,83,86,89} discussed the use of telerehabilitation for upper limb in stroke clients in home settings. The majority of these articles showed a positive impact of telerehabilitation on its usability and acceptance for post-stroke upper limb rehabilitation at home. Overall, in the majority of the studies, participants showed upper limb improvement in training programme tasks, clinical and kinematic outcomes.

Of these, eight articles^{19,21,27,41,55,65,69,83} reported the usability of a remote robotic telerehabilitation system. For instance, Rozevink et al. found users’ of a remote robotic system called HoMEcare aRm rehabiLItatioN that reported a high level of satisfaction, and the FMA-UE scores showed significant improvements in motor function from a baseline mean score of 36.3 to 43.1 after six-weeks of training.²⁷

Six articles^{37,48,59,64,71,74} reported the use of telecommunication in upper limb rehabilitation post-stroke. Three articles presented^48,59,64 a phone-monitored home exercise programme for upper limb rehabilitation following stroke. These studies confirmed the feasibility of the system and its positive impact on longitudinal improvement of motor function. The interventions rated high patient satisfaction and produced no adverse events. Furthermore, stroke patients provided high ratings of perceived usefulness and ease of use for remote mobile rehabilitation systems in home settings.³⁷

Nevertheless, studies have also shown that in-clinic therapy was more effective than home-based telerehabilitation interventions for post-stroke upper limb rehabilitation. Gauthier et al. reported whilst both home-based Tele-Gaming and Self-Gaming produced clinically meaningful Motor Activity Log Quality of Movement gains, home-based Self-Gaming was less effective than in-clinic constraint-induced therapy.⁷⁴

Games

Digital games have the potential to attract and engage stroke survivors with exercise whilst playing and subsequently supporting the rehabilitation process of the upper extremities. This can be achieved through repeated exercises of the hand and arm while the patient plays the game on a touchscreen or using a handheld device.⁷¹ Often, serious games are designed to support upper limb rehabilitation. Serious games can be understood as games primarily used for educational, rehabilitative or therapeutic purposes.⁹⁰ A high level of acceptance and satisfaction was reported on the use of a gesture-based serious game for motor rehabilitation managed through a web platform.⁴³

A total of 45 studies^{15,17–21,23,26–29,33,35,36,40,42,43,45,46,49,52–56,61–66,68,69,71,72,74,77–79,82–85,89,91} deployed games for home-based upper limb rehabilitation following stroke. Of these, 15 articles^{15,18,20,28,29,33,35,42,45,46,56,72,82,84,91} found a VR game to be a feasible, well-received, and safe intervention to use at home for people with chronic hemiparesis following stroke. These studies also demonstrated a positive impact of VR games on improving arm functioning and the number of reaching movements achieved. Likewise, three articles discussed the Nintendo Wii Sports™ for post-stroke upper limb rehabilitation.^18,62,66 Wii mote-mediated games perceived to be engaging in functional tasks and subsequently improved upper limb function.^18,62

Virtual, augmented, or mixed reality

VR can be described as a type of graphical user interface that displays a computer-generated immersive, three-dimensional, interactive environment that can be accessed and manipulated using, for example, stereo headphones, head-mounted stereo television goggles, and data-gloves.⁹² This emerging technology is widely being integrated in various medical fields.⁹³ Twenty-six articles^{12,15,18,20,28,29,33,35,36,38,40,42,45–47,51,56,58,69,72,78,79,82,84,89,91} used or created a VR intervention or a virtual environment for home-based upper limb rehabilitation after stroke.

Of these, six articles reported on the creation of a virtual environment for rehabilitation gaming exercise specifically for the upper extremities.^{29,47,56,79,82,89} Two articles^42,51 used a VR device (smart glove) for exercising the upper extremities as a rehabilitative intervention for stroke survivors.

Mobile technology

Twenty-two studies^{12,14,20,21,24,25,30,34,37,48,50,51,60,61,67,73–75,80,86,91,94} used a mobile technology or a mobile application as part of their intervention for home-based upper limb rehabilitation with stroke patients. Mobile technology had been used more frequently by stroke patients for information searching, and reminders in comparison to non-stroke participants.²⁵

In 5 studies,^{14,20,24,37,73} a mobile technology was used to quantify quality of movement and provide feedback to augment upper limb rehabilitation, and improve functional mobility in stroke patients remotely from home. The phone-guided home exercise programmes were also reported as an effective method of upper limb rehabilitation following stroke.⁴⁸ Two studies also used a smartphone app equipped with a machine learning algorithm and evaluated the effectiveness of a home-based rehabilitation system in chronic stroke survivors.^67,86 In the study authored by Fusari et al.,⁸⁶ from baseline to 14 days, mean FMA-UE decreased from 37.7 to 36.4, VAS increased from 0.8 to 2.8 and EQ-5DL-5L increased from 0.462 to 0.606. While patients’ activity correlated significantly with their daily activity target (R = 0.164), no significant correlation appeared with their weekly activity target (R = −0.035). Use of a mobile phone was also found to be feasible for a home-based, self-help telerehabilitation programme integrated with electromyography-driven wrist and/hand exoneuromusculoskeleton in stroke patients.^30,50

Sensors

Sensors were also used to detect the motion or movement of the upper extremities during the practice of rehabilitative interventions. Twenty-two articles^{15,16,18,21,22,26,31,34,39,45,46,50,51,54,57,58,61,78,82,84,87,89} discussed the use of upper extremity-sensing digital technologies in home rehabilitation of stroke patients.

Five studies^{22,26,31,50,58} using an electromyography-controlled biofeedback system that targeted wrist muscle activation, achieved a significant change on post-intervention surface electromyography outcomes. In these studies, the measures of upper limb movement and motor function, such as the WMFT, ARAT and FMA-UE, of the stroke patients showed significant increment during the follow-up. Likewise, in six studies,^{18,34,39,45,57,87} a wrist-worn system was effective in detecting upper limb movements.

Robotics

Prominent improvements in clinical outcomes were also evidenced in 8 studies^{27,41,49,55,69,79,83,85} that explored the effectiveness of a robotic device, using a computer-assisted arm rehabilitation by stroke survivors with upper limb weakness. In the majority of the studies that involve robotic interventions, significant clinical outcomes were recorded. For example, Radder et al.¹⁶ developed a wearable soft robotic glove to support grip and hand opening of affected fingers of stroke patients in a wide range of functional task performances. The system was found to be applicable, feasible and acceptable. However, according to Nijenhuis et al., a dynamic wrist and hand orthosis combined with a remotely monitored user interface and self-administered training in gaming environment showed no significant improvements as evidenced by the ARAT and MAL outcomes.¹⁹

Discussion

The aim of this review was to identify, map and synthesize the extent and nature of existing research literature on the use of home-based digital technology to support post-stroke upper limb rehabilitation. We found 76 papers that reported the use of digital technologies for post-stroke upper limb rehabilitation in home settings and six broad categories of technology emerged: Tele-rehabilitation, games, VR, sensor, mobile technology, and robotics. The aims of the majority of these studies were to develop, design and/or to assess feasibility, acceptability, user adherence and efficacy of a digital system for poststroke upper limb rehabilitation in home settings. Despite this, the methods applied to address aims across papers have been inconsistent. For instance, feasibility had been evaluated through participants’ concordance,^{32,35,48,51,81,86} clinical outcomes,^13,50 safety^13,15,32,45 and ease of use^16,28 to the given digital intervention.

In the majority of studies, the digital technology system used was multimodal and compromised of multiple digital devices delivered using laptops, desktops, tablets, and phone devices. Two categories of studies were found: Those that used technology to deliver an intervention focused on various stages of the patients’ rehabilitation journey ranging from assessment, therapy and monitoring; to those that focused upon the technological capabilities of the system development and evaluation.

Twenty-nine papers evaluated the use of telerehabilitation for post-stroke upper limb rehabilitation in home settings.^{19,21,23,24,26–28,30,35–37,41,44,48,50,53,55,59,64,65,69,71,72,74,79,81,83,86,89} The recent telecommunication advancements have boosted remote delivery of rehabilitation services via the internet of things, messaging services, telephone, videoconference, and other means of communication technology. Besides the convenience and flexibility of telerehabilitation, it has been reported to be advantageous in minimizing issues such as hospital acquired infection, fatigue and travel distance.⁹⁵ Telerehabilitation may also underpin an enhanced continuity of rehabilitation intervention, monitoring, and counselling from hospital to home for stroke survivors with upper limb impairment.^96,97 Furthermore, the dose and user adherence level to home-based digital rehabilitation might have better impact on the clinical outcomes. This can be observed on the study conducted by Benvenuti et al. where participants with high adherence to the telerehabilitation programme showed better upper limb improvement (WMFT, 9-Hole Peg Test) than those with a low adherence level.⁵⁹

The review also highlighted the popularity of VR as a technology being used for home-based rehabilitation of upper limb after stroke. More than 1/3 of the articles included have used or created VR or virtual environment as part of their intervention.^{12,15,18,20,28,29,33,35,36,38,40,42,45–47,51,56,58,69,72,78,79,82,84,89,91} VR enables the provision of rehabilitation offering rich experience and credibility in alternative environments such as at home. This minimizes auditory and visual distractions that possibly can happen in communal health care settings.⁹⁸ It could also stimulate the movements of upper limbs in stroke patients.⁹⁹ The augmentative or immersive nature of VR was suggested as being the reason for the enhancement of post-stroke upper limb recovery by encouraging more engaging and thus more intensive rehabilitation through daily use.^70,100

Even though most of the authors come to conclusion on the positive impact of mobile-based digital interventions for poststroke upper limb rehabilitation in home settings, Emmerson et al. could not find significant clinical outcome difference in its clinical trial that compared paper-based home exercise programme versus tablet-based home exercise, as indicated by the WMFT.⁶⁰ Of course, the application of a variety of digital interventions across studies could result in different degrees of clinical outcomes. Furthermore, the inconsistency in dose and period of interventions between studies might also influence the clinical outcomes.

Gamification, or game-based digital rehabilitation, is an advancing innovation with the potential to manage the limitations of in-person hospital-based rehabilitation. Patients have shown a high level of adherence, 97.8%, to gamification-mediated rehabilitative exercises.¹⁰¹ This review identified ^{45,15,17–21,23,26–29,33,35,36,40,42,43,45,46,49,52–56,61–66,68,69,71,72,74,77–79,82–85,89,91} studies in which various types of digital games had been deployed as part of the intervention for home-based post-stroke upper limb rehabilitation. These games were augmented with/without virtual/immersive reality, and played via computer, mobile phone, and other digital devices and the participants were able to engage in rehabilitation activities based upon gamification within the home setting.

Sensors had been used as supportive components of the digital technology systems for providing effective home-based rehabilitation services among people with stroke. Twenty-two articles discussed the use of upper extremity-sensing digital technologies in the home rehabilitation of stroke patients.^{15,16,18,21,22,26,31,34,39,45,46,50,51,54,57,58,61,78,82,84,87,89} Sensors do help with detecting body motions using physiological changes or physical stimulates.¹⁰² This way, they can help to assess and monitor the effectiveness of therapeutic exercise of upper extremities in stroke clients.¹⁰³

Robotics has also been applied in eight studies.^{27,41,49,55,69,79,83,85} Robots have been used for upper limb rehabilitation in several ways. One common approach was the use of robotic devices that guide the movement of a patient's arm through a range of motion. These devices had been programmed to provide varied levels of resistance and assistance to help the patient regain strength and flexibility. Another way that robots were used for upper limb rehabilitation was through VR environments. These environments can simulate real-world activities and provide patients with a safe and controlled environment to practice their movements. As a result, robots can enhance motor activities and improve the daily functioning of stroke patients.^49,50,65,69

This review was comprehensive and followed the Arksey and O’Malley steps.⁹ In line with scoping review methodology, the studies were not evaluated for clinical effectiveness, methodological rigour or for overall quality. However, the authors note that many of the studies had small sample sizes, used a range of different outcome measurements and few articles were included from Asia and no studies were found from Africa. This may challenge the conclusion that home-based digital post-stroke rehabilitation is applicable and acceptable to all, particularly those residing in low-and middle-income countries.

In conclusion, this large scoping review has demonstrated that the digital technologies used in post-stroke upper limb rehabilitation were multimodal, and system-based comprising telerehabilitation, gamification, VR, mobile technology, sensors and robotics. The technologies were primarily used to assess, monitor, evaluate or provide therapeutic interventions for post-stroke upper limb rehabilitations within the home setting.

Furthermore, many aspects of technology use in rehabilitation, such as, whether home-based digital rehabilitation has advantages over, or is as effective as, hospital rehabilitation, in terms of treatment gains, adherence, patient satisfaction and cost, require further evaluation through interventional large-scale studies.

Clinical messages

A wide range of digital technologies have been used in upper limb rehabilitation post-stroke incorporated at various stages along the patient journey including assessment, intervention, monitoring, and evaluation.

Studies suggest the implementation of home-based digital rehabilitation as supporting the motor recovery of the upper limb is feasible and acceptable to the user with high ratings of perceived usefulness.

Few studies explored key questions such as barriers to uptake and whether use of digital technology was more effective than in-person rehabilitation thus larger, more methodologically robust studies using a core outcome set to enable comparisons are required.

Supplemental Material

sj-docx-1-cre-10.1177_02692155231189257 - Supplemental material for The use of home-based digital technology to support post-stroke upper limb rehabilitation: A scoping review

Supplemental material, sj-docx-1-cre-10.1177_02692155231189257 for The use of home-based digital technology to support post-stroke upper limb rehabilitation: A scoping review by Gdiom Gebreheat, Adele Goman and Alison Porter-Armstrong in Clinical Rehabilitation

Supplemental Material

sj-docx-2-cre-10.1177_02692155231189257 - Supplemental material for The use of home-based digital technology to support post-stroke upper limb rehabilitation: A scoping review

Supplemental material, sj-docx-2-cre-10.1177_02692155231189257 for The use of home-based digital technology to support post-stroke upper limb rehabilitation: A scoping review by Gdiom Gebreheat, Adele Goman and Alison Porter-Armstrong in Clinical Rehabilitation

Supplemental Material

sj-docx-3-cre-10.1177_02692155231189257 - Supplemental material for The use of home-based digital technology to support post-stroke upper limb rehabilitation: A scoping review

Supplemental material, sj-docx-3-cre-10.1177_02692155231189257 for The use of home-based digital technology to support post-stroke upper limb rehabilitation: A scoping review by Gdiom Gebreheat, Adele Goman and Alison Porter-Armstrong in Clinical Rehabilitation

Footnotes

Author contribution

All authors meet the criteria for authorship.

Declaration of conflicting interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The lead author is funded by a PhD studentship awarded by Edinburgh Napier University, Scotland. No other financial support has been provided for this research.

ORCID iD

Gdiom Gebreheat

Supplemental material

Supplemental material for this article is available online.

References

. Post-stroke rehabilitation cost with traditional therapy: Evidence from a public hospital. JPMA J Pak Med Assoc 2019; 69: S87–S95.

Langhorne

Bernhardt

Kwakkel

. Stroke rehabilitation. The Lancet 2011; 377: 1693–1702.

WHO. Rehabilitation [Internet]. 2022 [cited 2022 Jul 13], https://www.who.int/news-room/fact-sheets/detail/rehabilitation

Ghulam

Shah

Nogueras

, et al. The COVID-19 pandemic: A pandemic of lockdown loneliness and the role of digital technology. J Med Internet Res 2020; 22: 22287.

Meyer

De Bruyn

Lafosse

, et al. Somatosensory impairments in the upper limb poststroke: Distribution and association with motor function and visuospatial neglect. Neurorehabil Neural Repair 2016; 30: 731–742.

Stinear

Byblow

Ackerley

, et al. Proportional motor recovery after stroke: Implications for trial design. Stroke 2017; 48: 795–798.

JPO

Liu

Ting

DSJ

, et al. Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective. Prog Retinal Eye Res 2020; 82: 1–33.

Ofcom. A review of Ofcom’s research on digital exclusion among adults in the UK, 2022.

Arksey

O’Malley

. Scoping studies: Towards a methodological framework. Int J Soc Res Methodol: Theory Pract 2005; 8: 19–32.

10.

Tricco

Lillie

Zarin

, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and explanation. Ann Intern Med 2018; 169: 467–473.

11.

Braun

Clarke

. Using thematic analysis in psychology. Qual Res Psychol 2006; 3: 77–101.

12.

Wilson

Rogers

Vogel

, et al. Home-based (virtual) rehabilitation improves motor and cognitive function for stroke patients: a randomized controlled trial of the elements (EDNA-22) system. J Neuroeng Rehabil 2021; 18: 165.

13.

Zondervan

Friedman

Chang

, et al. Home-based hand rehabilitation after chronic stroke: Randomized, controlled single-blind trial comparing the MusicGlove with a conventional exercise program. J Rehabil Res Dev 2016; 53: 457–472.

14.

Langan

Bhattacharjya

Subryan

, et al. In-Home rehabilitation using a smartphone app coupled with 3D printed functional objects: Single-subject design study. JMIR Mhealth Uhealth 2020; 8: e19582.

15.

Wittmann

Held

Lambercy

, et al. Self-directed arm therapy at home after stroke with a sensor-based virtual reality training system. J Neuroeng Rehabil 2016; 13: 75.

16.

Radder

Prange-Lasonder

Kottink

AIR

, et al. Feasibility of a wearable soft-robotic glove to support impaired hand function in stroke patients. J Rehabil Med 2018; 50: 598–606.

17.

Parker

Mawson

Mountain

, et al. Stroke patients’ utilisation of extrinsic feedback from computer-based technology in the home: a multiple case study realistic evaluation. BMC Med Inform Decis Mak 2014; 14: 1–13.

18.

Lin

Kelleher

Engsberg

. Developing home-based virtual reality therapy interventions. Games Health J 2013; 2: 34–38.

19.

Nijenhuis

Prange

Amirabdollahian

, et al. Feasibility study into self-administered training at home using an arm and hand device with motivational gaming environment in chronic stroke. J Neuroeng Rehabil 2015; 12: 89.

20.

Choi

Paik

. Mobile game-based virtual reality program for upper extremity stroke rehabilitation. J Vis Exp 2018; 133: e56241. doi:10.3791/56241

21.

Linder

Rosenfeldt

Bay

, et al. Improving quality of life and depression after stroke through telerehabilitation. Am J Occup Therapy: Official Publication Amer Occup Therapy Assoc 2015; 69: 6902290020p1–10.

22.

Brown

EVD

McCoy

Fechko

, et al. Preliminary investigation of an electromyography-controlled video game as a home program for persons in the chronic phase of stroke recovery. Arch Phys Med Rehabil 2014; 95: 1461–1469.

23.

Cramer

Dodakian

, et al. Efficacy of home-based telerehabilitation vs in-clinic therapy for adults after stroke: A randomized clinical trial. JAMA Neurol 2019; 76: 1079–1087.

24.

Langan

Delave

Phillips

, et al. Home-based telerehabilitation shows improved upper limb function in adults with chronic stroke: A pilot study. J Rehabil Med 2013; 45: 217–220.

25.

Kim

. Attitudes and use patterns for mobile technology and upper extremity home exercises in stroke survivors in the United States. Br J Occup Ther 2022; 85(9): 677–684. doi:10.1177/03080226211070564

26.

Buick

Kowalczewski

Carson

, et al. Tele-Supervised FES-assisted exercise for hemiplegic upper limb. IEEE Transactions on Neural Systems and Rehabilitation Engineering : A Publication of the IEEE Engineering in Medicine and Biology Society 2016; 24: 79–87.

27.

Rozevink

van der Sluis

Garzo

, et al. HoMEcare aRm rehabiLItatioN (MERLIN): Telerehabilitation using an unactuated device based on serious games improves the upper limb function in chronic stroke. J Neuroeng Rehabil 2021; 18: 48.

28.

Jonsdottir

Baglio

Gindri

, et al. Virtual reality for motor and cognitive rehabilitation from clinic to home: A pilot feasibility and efficacy study for persons with chronic stroke. Front Neurol 2021; 12: 1–12.

29.

Triandafilou

Tsoupikova

Barry

, et al. Development of a 3D, networked multi-user virtual reality environment for home therapy after stroke. J Neuroeng Rehabil 2018; 15: 88.

30.

Bernocchi

Vanoglio

Baratti

, et al. Home-based telesurveillance and rehabilitation after stroke: A real-life study. Top Stroke Rehabil 2016; 23: 106–115.

31.

Nam

Rong

, et al. An exoneuromusculoskeleton for self-help upper limb rehabilitation after stroke. Soft Robotics 2022; 9: 14–35.

32.

Bernocchi

Mulè

Vanoglio

, et al. Home-based hand rehabilitation with a robotic glove in hemiplegic patients after stroke: A pilot feasibility study. Top Stroke Rehabil 2018; 25: 114–119.

33.

Fluet

Sciences

Merians

, et al. Robotic/virtual reality intervention program individualized to meet the specific sensorimotor impairments of an individual patient: a case study. Int J Disabil Hum Dev 2014; 13: 401–407.

34.

Lee

Adans-Dester

Grimaldi

, et al. Enabling stroke rehabilitation in home and community settings: A wearable sensor-based approach for upper-limb motor training. IEEE J Transl Eng Health Med 2018; 6: 1–11. doi:10.1109/JTEHM.2018.2829208

35.

Burdea

Grampurohit

Kim

, et al. Feasibility of integrative games and novel therapeutic game controller for telerehabilitation of individuals chronic post-stroke living in the community. Top Stroke Rehabil 2020; 27: 321–336.

36.

Linder

Reiss

Buchanan

, et al. Incorporating robotic-assisted telerehabilitation in a home program to improve arm function following stroke: a case study. J Neurology Physical Therapy 2013; 37: 125–132.

37.

Bhattacharjya

Cavuoto

Reilly

, et al. Usability, usefulness, and acceptance of a novel, portable rehabilitation system (mRehab) using smartphone and 3D printing technology: Mixed methods study. JMIR Human Factors 2021; 8: e21312.

38.

Ballester

Nirme

Camacho

, et al. Domiciliary VR-based therapy for functional recovery and cortical reorganization: randomized controlled trial in participants at the chronic stage post stroke. JMIR Serious Games 2017; 5: e15.

39.

Adans-Dester

Fasoli

Fabara

, et al. Can kinematic parameters of 3D reach-to-target movements be used as a proxy for clinical outcome measures in chronic stroke rehabilitation? An exploratory study. J Neuroeng Rehabil 2020; 17: 106.

40.

Standen

Threapleton

Connell

, et al. Patients’ use of a home-based virtual reality system to provide rehabilitation of the upper limb following stroke. Phys Ther 2015; 95: 350–359.

41.

Amirabdollahian

Ates

Basteris

, et al. Design, development and deployment of a hand/wrist exoskeleton for home-based rehabilitation after stroke – SCRIPT project. ROBOTICA 2014; 32: 1331–1346.

42.

Standen

Threapleton

Richardson

, et al. A low cost virtual reality system for home based rehabilitation of the arm following stroke: A randomised controlled feasibility trial. Clin Rehabil 2017; 31: 340–350.

43.

Baluz

Teles

Fontenele

, et al. Motor rehabilitation of upper limbs using a gesture-based serious game: Evaluation of usability and user experience. Games Health J 2022; 11: 1–9.

44.

Uswatte

Taub

Lum

, et al. Tele-rehabilitation of upper-extremity hemiparesis after stroke: Proof-of-concept randomized controlled trial of in-home constraint-induced movement therapy. Restor Neurol Neurosci 2021; 39: 303–318.

45.

Borstad

Crawfis

Phillips

, et al. In-Home delivery of constraint-induced movement therapy via virtual reality gaming. J Patient Cent Res Rev 2018; 5: 6–17.

46.

Qiu

Cronce

Patel

, et al. Development of the home based virtual rehabilitation system (HoVRS) to remotely deliver an intense and customized upper extremity training. J Neuroeng Rehabil 2020; 17: 155.

47.

Thielbar

Triandafilou

Barry

, et al. Home-based upper extremity stroke therapy using a multi-user virtual reality environment: A randomized trial. Arch Phys Med Rehabil 2020; 101: 196–203.

48.

Simpson

Eng

Chan

. H-GRASP: the feasibility of an upper limb home exercise program monitored by phone for individuals post stroke. Disabil Rehabil 2017; 39: 874–882.

49.

Sivan

Gallagher

Makower

, et al. Home-based computer assisted arm rehabilitation (hCAAR) robotic device for upper limb exercise after stroke: results of a feasibility study in home setting. J Neuroeng Rehabil 2014; 11: 163.

50.

Nam

Zhang

Chow

, et al. Home-based self-help telerehabilitation of the upper limb assisted by an electromyography-driven wrist/hand exoneuromusculoskeleton after stroke. J Neuroeng Rehabil 2021; 18: 137.

51.

Lansberg

Legault

MacLellan

, et al. Home-based virtual reality therapy for hand recovery after stroke. PM & R: J Inj, Funct Rehabil 2022; 14: 320–328.

52.

Slijper

Svensson

Backlund

, et al. Computer game-based upper extremity training in the home environment in stroke persons: a single subject design. J Neuroeng Rehabil 2014; 11: 1–8.

53.

Dodakian

McKenzie

, et al. A home-based telerehabilitation program for patients with stroke. Neurorehabil Neural Repair 2017; 31: 923–933.

54.

Bai

Song

. Development of a novel home based multi-scene upper limb rehabilitation training and evaluation system for post-stroke patients. IEEE Access 2019; 7: 9667–9677.

55.

Guillen-Climent

Garzo

Munoz-Alcaraz

, 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 Neuroeng Rehabil 2021; 18: 41.

56.

Thielbar

Spencer

Tsoupikova

, et al. Utilizing multi-user virtual reality to bring clinical therapy into stroke survivors’ homes. J Hand Ther : Off J Am Soc Hand Therap 2020; 33: 246–253.

57.

Tsai

Wang

Zariffa

. Identifying hand use and hand roles after stroke using egocentric video. IEEE J Transl Eng Health Med 2021; 9: 1–10.

58.

Ellis

Hancock

Kennedy

, et al. Consideration-of-concept of EvolvRehab-body for upper limb virtual rehabilitation at home for people late after stroke. Physiotherapy 2022; 116: 97–107.

59.

Benvenuti

Stuart

Cappena

, et al. Community-based exercise for upper limb paresis: A controlled trial with telerehabilitation. Neurorehabil Neural Repair 2014; 28: 611–620.

60.

Emmerson

Harding

Taylor

. Home exercise programmes supported by video and automated reminders compared with standard paper-based home exercise programmes in patients with stroke: A randomized controlled trial [with consumer summary]. Clin Rehabil 2017; 31: 1068–1077.

61.

Bellomo

Paolucci

Saggino

, et al. The WeReha project for an innovative home-based exercise training in chronic stroke patients: A clinical study. J Cent Nerv Syst Dis 2020; 12: 1–12.

62.

Wingham

Adie

Turner

, et al. Participant and caregiver experience of the Nintendo wii Sports^TM after stroke: Qualitative study of the trial of Wii^TM in stroke (TWIST). Clin Rehabil 2015; 29: 295–305.

63.

Brown

Dudgeon

Gutman

, et al. Understanding upper extremity home programs and the use of gaming technology for persons after stroke. Disabil Health J 2015; 8: 507–513.

64.

Szturm

Imran

Pooyania

, et al. Evaluation of a game based tele rehabilitation platform for in-home therapy of hand-arm function post stroke: Feasibility study. PM and R 2021; 13: 45–54.

65.

Wolf

Sahu

Bay

, et al. The HAAPI (home arm assistance progression initiative) trial: a novel robotics delivery approach in stroke rehabilitation. Neurorehabil Neural Repair 2015; 29: 958–968.

66.

Adie

Schofield

Berrow

, 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: 173–185.

67.

Chae

Kim

Lee

, et al. Development and clinical evaluation of a web-based upper limb home rehabilitation system using a smartwatch and machine learning model for chronic stroke survivors: Prospective comparative study. JMIR Mhealth Uhealth 2020; 8: e17216.

68.

King

Hijmans

Sampson

, et al. Home-based stroke rehabilitation using computer gaming. New Zealand J Physiother 2012; 40: 128–134.

69.

Song

, et al. One-Therapist to three-patient telerehabilitation robot system for the upper limb after stroke. Int J Soc Robot 2016; 8: 319–329.

70.

Kim

Cho

, et al. Clinical medicine clinical application of virtual reality for upper limb motor rehabilitation in stroke: review of technologies and clinical evidence. J Clin Med 2020; 9: 1–20.

71.

Chen

Zheng

, et al. A qualitative study on user acceptance of a home-based stroke telerehabilitation system. Top Stroke Rehabil 2020; 27: 81–92.

72.

Allegue

Kairy

Higgins

, et al. A personalized home-based rehabilitation program using exergames combined with a telerehabilitation app in a chronic stroke survivor: mixed methods case study. JMIR Ser Games 2021; 9: e26153.

73.

Bhattacharjya

Stafford

Cavuoto

, et al. Harnessing smartphone technology and three dimensional printing to create a mobile rehabilitation system, mRehab: assessment of usability and consistency in measurement. J Neuroeng Rehabil 2019; 16: 127.

74.

Gauthier

Nichols-Larsen

Uswatte

, et al. Video game rehabilitation for outpatient stroke (VIGoROUS): A multi-site randomized controlled trial of in-home, self-managed, upper-extremity therapy. EClinicalMedicine 2022; 43: 1–12.

75.

Segura

Grau-Sánchez

Sanchez-Pinsach

, et al. Designing an app for home-based enriched music-supported therapy in the rehabilitation of patients with chronic stroke: A pilot feasibility study. Brain Inj 2021; 35: 1585–1597.

76.

Ghorbel

Baptista

Shabayek

, et al. Home-Based rehabilitation system for stroke survivors: a clinical evaluation. J Med Syst 2020; 44: 1–11.

77.

Prange-Lasonder

Radder

Kottink

AIR

, et al. Applying a soft-robotic glove as assistive device and training tool with games to support hand function after stroke: Preliminary results on feasibility and potential clinical impact. Int Conf Rehabilitation Robotics 2017; 2017: 1401–1406.

78.

Seo

Arun Kumar

Hur

, et al. Usability evaluation of low-cost virtual reality hand and arm rehabilitation games. J Rehabil Res Dev 2016; 53: 321–334.

79.

Pareto

Johansson

Zeller

, et al. Virtual TeleRehab: A case study. Stud Health Technol Inform 2011; 169: 676–680.

80.

McCormick

Ireland

Yohannes

, et al. Technology-Dependent rehabilitation involving action observation and movement imagery for adults with stroke: Can it work? Feasibility of self-led therapy for upper limb rehabilitation after stroke. Stroke Res Treat 2022; 2022: 1–10.

81.

Zhang

Yan

Sun

, et al. Effects of coaching-based teleoccupational guidance for home-based stroke survivors and their family caregivers: A pilot randomised controlled trial. Evidence-Based Complementary Altern Med 2022; 19: 1–13.

82.

Proffitt

Skubic

. Novel clinically-relevant assessment of upper extremity movement using depth sensors. Top Stroke Rehabil 2023; 30: 11–20.

83.

Spits

Rozevink

Balk

, et al. Stroke survivors’ experiences with home-based telerehabilitation using an assistive device to improve upper limb function: A qualitative study. Disability and Rehabilitation: Assistive Technology 2022: 1–9. doi:10.1080/17483107.2022.2120641

84.

Hernandez

Bubyr

Archambault

, et al. Virtual reality–based rehabilitation as a feasible and engaging tool for the management of chronic poststroke upper-extremity function recovery: Randomized controlled trial. JMIR Serious Games 2022; 10: e37506.

85.

Gallagher

Sivan

Levesley

. Making best use of home-based rehabilitation robots. Appl Sci (Switzerland) 2022; 12: 1–15.

86.

Fusari

Gibbs

Hoskin

, et al. What is the feasibility and patient acceptability of a digital system for arm and hand rehabilitation after stroke? A mixed-methods, single-arm feasibility study of the “OnTrack” intervention for hospital and home use. BMJ open 2022; 12: e062042.

87.

Seim

Wolf

Starner

. Wearable vibrotactile stimulation for upper extremity rehabilitation in chronic stroke: Clinical feasibility trial using the VTS glove. J Neuroeng Rehabil 2021; 18: 14.

88.

Shem

Irgens

Alexander

. Getting Started: Mechanisms of Telerehabilitation, 2022. doi:10.1016/B978-0-323-82486-6.00002-2

89.

Qiu

Cronce

Fluet

, et al. Home-based virtual rehabilitation for upper extremity functional recovery post-stroke. J Altern Med Res 2018; 10: 27–35.

90.

Hocine

Gouaïch

Cerri

, et al. Adaptation in serious games for upper-limb rehabilitation: an approach to improve training outcomes. User Model User-Adapt Interact 2015; 25: 65–98.

91.

Kilbride

Scott

DJM

Butcher

, et al. Safety, feasibility, acceptability and preliminary effects of the neurofenix platform for rehabilitation via HOMe based gaming exercise for the upper-limb post stroke (RHOMBUS): Results of a feasibility intervention study. BMJ Open 2022; 12: 52555.

92.

Brey

Søraker

. Philosophy of computing and information technology. Philos Technol Eng Sci 2009: 1341–1407. doi:10.1016/B978-0-444-51667-1.50051-3

93.

Pawassar

Tiberius

. Virtual reality in health care: bibliometric analysis. JMIR Serious Games 2021; 9: 1–19.

94.

Rozevink

van der Sluis

Hijmans

. HoMEcare aRm rehabiLItatioN (MERLIN): preliminary evidence of long term effects of telerehabilitation using an unactuated training device on upper limb function after stroke. J Neuroeng Rehabil 2021; 18: 141.

95.

Buckingham

Anil

Demain

, et al. Telerehabilitation for people with physical disabilities and movement impairment: A survey of United Kingdom practitioners. JMIRx Med 2022; 3: 1–16.

96.

Marin-Pardo

Phanord

Donnelly

, et al. Development of a low-cost, modular muscle-computer interface for at-home telerehabilitation for chronic stroke. Sensors 2021; 21: 1–15.

97.

Agostini

Moja

Banzi

, et al. Telerehabilitation and recovery of motor function: A systematic review and meta-analysis. J Telemed Telecare 2015; 21: 202–213.

98.

Weber

Nilsen

Gillen

, et al. Immersive virtual reality mirror therapy for upper limb recovery following stroke: A pilot study. Am J Phys Med Rehabil 2019; 98: 783–788.

99.

Mekbib

Debeli

Zhang

, et al. A novel fully immersive virtual reality environment for upper extremity rehabilitation in patients with stroke. Ann N Y Acad Sci 2021; 1493: 75–89.

100.

Zhang

Liu

, et al. Virtual reality for limb motor function, balance, gait, cognition and daily function of stroke patients: A systematic review and meta-analysis. J Adv Nurs 2021; 77: 3255–3273.

101.

Seo

Barry

Ghassemi

, et al. Use of an EMG-controlled game as a therapeutic tool to retrain hand muscle activation patterns following stroke: a pilot study. J Neurol Phys Ther: JNPT 2022; 46: 198–205.

102.

Nascimento

Bonfati

Freitas

, et al. Sensors and systems for physical rehabilitation and health monitoring-A review. Sensors 2020; 20: 1–28.

103.

Krausz

Hargrove

. A survey of teleceptive sensing for wearable assistive robotic devices. Sensors 2019; 19: 1–27.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.02 MB

0.06 MB

0.11 MB