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Abstract

Background

The clinical adoption of telerehabilitation accelerated rapidly over the last few years, creating opportunities for clinicians and researchers to explore the use of digital technologies and telerehabilitation in the assessment of deficits related to neurological conditions. The objectives of this scoping review were to identify outcome measures used to remotely assess the motor function and participation in people with neurological conditions and report, when available, the psychometric data of these remote outcome measures.

Methods

MEDLINE (Ovid), CINAHL, PubMed, PsychINFO, EMBASE, and Cochrane databases were searched between December 13, 2020, and January 4, 2021, for studies investigating the use of remote assessments to evaluate motor function and participation in people with neurological conditions. An updated search was completed on May 9, 2022, using the same databases and search terms. Two reviewers independently screened each title and abstract, followed by full-text screening. Data extraction was completed using a pre-piloted data extraction sheet where outcome measures were reported as per the International Classification of Functioning, Disability and Health.

Results

Fifty studies were included in this review. Eighteen studies targeted outcomes related to body structures and 32 targeted those related to activity limitation and participation restriction. Seventeen studies reported psychometric data; of these, most included reliability and validity data.

Conclusion

Clinical assessments of motor function of people living with neurological conditions can be completed in a telerehabilitation or remote context using validated and reliable remote assessment measures.

Keywords

telerehabilitation remote assessment outcome measures neurology rehabilitation

Introduction

Digital health is commonly used as an umbrella term describing the use of telecommunication technology within the healthcare context.¹ In rehabilitation, digital health may involve the use of information and communication technology such as smartphone applications, wearable sensors, activity tracking devices, and telehealth platforms to assess, monitor, or deliver care. Telerehabilitation is defined as the use of communication technology to provide rehabilitation services including clinical assessments, health monitoring, and interventions.² Although the adoption of telerehabilitation has increased dramatically over the last few years, rehabilitation professionals still report challenges associated with the uptake of telerehabilitation,^3,4 documenting barriers such as remote assessment.

Outcome measures that are valid and reliable when used in remote assessments are necessary when documenting various motor deficits including balance, mobility, and upper extremity function. Currently, research has documented the psychometric properties of musculoskeletal assessments administered remotely (e.g., Oswestry Disability Index, goniometry)^5,6; however, the lack of validated remote assessment measures for neurological deficits poses barriers to telerehabilitation implementation in neurological settings by limiting the ability of clinicians to objectively and accurately complete clinical assessment remotely.⁷ Authors have reported that remote monitoring and remote assessment are more difficult than in-person assessment due to perceived safety risks, limited space in home settings, or the necessity of caregiver presence.^8–10 Although the evidence is growing rapidly for the validity of such remote assessments,^7,11,12 additional research is necessary to identify new validated clinical tools.

The objectives of this scoping review were to: i) identify the remote outcome measures being used in the assessment of people with neurological conditions (PwN) including the description of the neurological conditions and the clinician characteristics, ii) describe, as per the International Classification of Functioning, Disability and Health (ICF), how these remote outcome measures were being used, and iii) report the psychometric data (e.g., validity, reliability, normative values) when available.

Methods

Systematic search

The complete scoping review methodology is reported in O’Neil et al.¹³ Briefly, following the methodology proposed by Arksey and O’Malley and adapted by Colquhoun et al.,^14,15 an initial systematic search of MEDLINE (Ovid), CINAHL, PubMed, PSychINFO, EMBASE, and Cochrane (Appendix 1) was completed between December 13, 2020, and January 4, 2021. The search was updated on May 9, 2022. Specific search criteria were used to identify clinical outcome measures of motor deficits and function limitation of PwN (i.e., gait, balance, mobility, strength) conducted using telerehabilitation platforms including virtual reality, mHealth, videoconferencing, and digital health smart applications. Using Covidence online application (Melbourne, Australia), two reviewers (KB and EMD) independently screened article titles and abstracts to identify those meeting the inclusion criteria; two pairs of reviewers screened each article (KB, EMD, and JO). Full-text reviews were then completed by two reviewers (KB and JO) for articles meeting the review objectives. Conflicts were resolved by a fourth reviewer (LS) before moving to the next step. Interrater reliability between the three reviewers was assessed using Cohen's κ. The reference lists of all reviews and meta-analyses identified during screening were manually searched for additional articles. Since this scoping review did not involve human participants, participant consent was not necessary for this study.

Data extraction

Two reviewers (JO and KB) extracted data from the included articles by categorizing the information into four main domains: i) general information (i.e., study design, sample size, clinician administering the assessment, and neurological condition); ii) outcome measures as per the ICF classifications of body function (i.e., physiological body functions) and activity and participation (i.e., task execution and life involvement); iii) teleplatforms used; and iv) psychometric data (i.e., validity, reliability, and normative data). The level of evidence for included studies was rated, when possible, using the method presented by Butler et al.¹⁶

Results

Included studies

The systematic search identified a total of 687 articles. Two hundred and ninety-three articles were found during the initial search in 2020/2021 and a further 387 in 2022. After hand-searching the reference lists of reviews and meta-analyses identified during screening, an additional 7 articles were added. Of the 687 articles, 364 duplicates were removed, and 323 titles and abstracts were screened. Of the 323 articles screened, 167 were excluded, leaving 156 full articles to be reviewed. A further 106 articles were excluded once the full articles were reviewed, leaving 50 studies on the use of remote assessments for people living with various neurological conditions (Figure 1). Of these 50 studies, seven were among those added following the updated search, which suggested an increased interest in remote assessment since the onset of the COVID-19 pandemic. Interrater reliability for full article selection was very high (k = 0.86) between reviewers KB and EMD and high (k = 0.80) between JO and KB.¹⁷

Figure 1.

PRISMA flow chart reporting on study identification, screening, inclusion, and exclusion of studies (www.prisma-statment.org).

Objective 1: Identification of the neurological conditions assessed and the characteristics of the clinicians

The studies identified included those investigating remote assessment for people living with stroke (n = 17),^11,18–34 Parkinson's disease (n = 16),^35–51 traumatic brain injury including concussion (n = 8),^52–59 multiple sclerosis (n = 6),^60–64 and spinal cord injury (n = 3)^65–67 (Table 1).

Table 1.

Description of included studies.

References	Sample size	Design	Level of evidence	Population	Healthcare provider
Jack et al. 2001	n = 3	Pilot	V	Stroke	OT, PT
Zhang et al., 2001	n = 60 (30 with brain injury, 30 without)	Group comparison (patient to control)	II	Traumatic brain injury	Rehabilitation therapist
Theodoros et al. 2003	n = 10	Pilot	V	Acquired brain injury	SLP
Broeren et al., 2006	n = 0	Development of system	N/A	Stroke	Did not report
Fisher et al. 2007	n = 15	Pilot	V	Stroke	OT
Leocani et al., 2007	n = 24 (12 MS and 12 controls)	Group comparison (patient to control)	II	Multiple sclerosis	PT
Broeren et al., 2007	n = 5	Experimental, single-subject repeated design (AB)	IV	Stroke	OT
Giansanti et al. 2008	n = 3 (healthy)	Description of system and potential use	V	Parkinson's disease	Did not report
Kane et al. 2008	n = 20	Pilot	V	Multiple sclerosis	Physician's assistant with neurology experience and neurologist
Westin et al. 2010	n = 65	Description of system and potential use	V	Parkinson's disease	Nursing
Kowalczewski et al. 2011	n = 13	Experimental, RCT	I	Spinal cord injury	PT
Rábago et al. 2011	n = 1	Exploratory, case study	V	Traumatic brain injury (concussion)	PT
Wang et al. 2011	n = 54 (29 with PD and 25 controls)	Group comparison (patient to control), repeated measures	II	Parkinson's disease	Did not report
Arias et al. 2012	n = 34 (12 healthy young subjects, 12 elderly controls, 10 with PD)	Group comparison (patient to control), repeated measures	II	Parkinson's disease	Did not report
Combs et al. 2012	n = 9	Single-group cohort (pre-test/post-test)	IV	Stroke	Did not report
Cuthbert 2012	n = 20	Experimental, RCT (doctoral thesis)	I	Traumatic brain injury	PT
Westin et al. 2012	n = 30	Usability/Validation study	V	Parkinson's disease	Neurologist and neuropsychologist
Gilat et al., 2013	n = 28 (17 with freezing gait and 11 non-freezing gait)	Experimental, single-subject repeated measures design	IV	Parkinson's disease	Did not report
Thielbar et al., 2014	n = 16	Feasibility/Preliminary	V	Stroke	OT
Trincado-Alonso et al., 2014	n = 15	Feasibility	V	Spinal cord injury	Therapist
Arora et al., 2015	n = 20 (10 with PD and 10 controls)	Pilot	V	Parkinson's disease	Movement disorder specialist
Killane et al. 2015	n = 30 (13 with FOG and 7 without FOG)	Before and after intervention	III	Parkinson's disease	Movement disorder specialist
Sessoms et al. 2015	n = 25 (10 with mTBI and 15 health controls)	Group comparison (patient to control)	II	Traumatic brain injury (concussion)	PT program
Sola-Valls et al. 2015	n = 23	Longitudinal study over one year	V	Multiple sclerosis	Neurological examination
Sungmin et al. 2016	n = 9	Description and feasibility	V	Stroke	Did not report
Tobler-Ammann et al., 2016	n = 31	Validation	V	Stroke	OT
Trinh et al. 2016	n = 46	Dose-matched intervention study	IV	Stroke	Exercise physiologist
Wittmann 2016	n = 11	Single-group cohort	IV	Stroke	PT
Luque-Moreno et al., 2016	n = 2	Before and after intervention	III	Stroke	PT
Cubo et al., 2017	n = 40	Prospective randomized case–control	III	Parkinson's disease	Neurologist
Perez-Marcos et al., 2017	n = 10	Pilot	V	Stroke	PT
Vargas et al. 2017	n = 11 (male suspected concussion)	Feasibility	V	Traumatic brain injury (concussion)	Neurologist
Wright et al. 2017	n = 72 (14 with mTBI, 58 controls)	Group comparison (patient to control)	III	Traumatic brain injury (concussion)	Athletic trainer or PT
Finkelstein et al., 2018	n = 10	Feasibility	V	Multiple ssclerosis	PT and exercise physiologist
Salisbury et al. 2018	n = 42	Proof of concept study	N/A	Traumatic brain injury (concussion)	Did not report
Wall et al. 2018	n = 30	Feasibility	V	Multiple sclerosis	PT
Horin et al., 2019	n = 37 (17 intervention, 20 control)	Experimental, RCT	I	Parkinson's disease	OT, PT, SLP
Hung et al., 2019	n = 33	Experimental, RCT	I	Stroke	OT
Lheureux et al., 2020	n = 10	Pilot	V	Parkinson's disease	Did not report
Maillart et al. 2020	n = 46	Experimental, RCT	I	Multiple sclerosis	Neurologist
Ona et al. 2020	n = 20	Pilot	V	Parkinson's disease	Did not report
Thielbar et al., 2020	n = 20 (24 enrolled)	Experimental, RCT	I	Stroke	Research therapist
de Carvalho et al. 2021	n = 34	Observational/validation	V	Parkinson's disease	Trained researchers
Hawkins et al. 2021	n = 80 (40 with PD, 40 control)	Group comparison (patient to control)	III	Parkinson's disease	Did not report
Peters et al. 2021	n = 5	Pilot	V	Stroke	PT
de Rooij et al. 2021	n = 28	Experimental, RCT	I	Stroke	PT
Szturm et al. 2021	n = 10	Feasibility	V	Stroke	Did not report
An & Park, 2022	n = 40	Experimental, RCT	I	Spinal cord injury	PT
Borzì et al. 2022	n = 31	Description and use of technology	V	Parkinson's disease	Neurologist
Safarpour et al. 2022	n = 31	Feasibility	V	Parkinson's disease	Did not report

Note: OT: occupational therapist; PT: physiotherapist; SLP: speech language pathologist; RCT: randomized controlled trials.

Half of the studies (n = 25) included in this review were Level V evidence (i.e., case studies) and only eight were Level I evidence (i.e., RCT). Only 37 studies reported on the profession of the clinician completing the remote assessments; these included occupational therapists, nurses, movement disorder specialists, physiotherapists, speech-language pathologists, neurologists, athletic trainers, and exercise physiologists.

Objective 2: Remote outcome measures described using the International Classification of Functioning, Disability and Health

Body structures

Eighteen studies investigated how remote outcome measures were implemented and used to assess deficits specific to body structure impairments. Investigations included the remote assessment of upper extremity motor impairments (n = 5), overall changes in range of motion, strength, or speed of muscle contraction (n = 5), condition-specific neurological impairment assessment (n = 4), lower extremity impairments (n = 2), and postural instability (n = 2). Teleplatforms included were virtual reality coupled with wearable sensors, asynchronous and synchronous telerehabilitation administered via videoconferencing, mobile applications, and home-motion sensors (Table 2A).

Table 2A.

Remote assessments used to assess body structure impairments in various neurological conditions, following the International Classification of Functioning, Disability and Health.

References	Clinical assessment targeting body impairments	Telerehabilitation platforms
References	Clinical assessment targeting body impairments	Telerehabilitation platforms	Stroke
Jack et al. 2001	Range of motion Speed and strength of movement Finger fractionation	Virtual reality and wearable sensor: PC-based rehabilitation workstation (Pentium II 400 MHz PC with a FireGL 4000 graphics accelerator and two input–output gloves: CyberGlove and the Rutgers Master II-ND)
Fischer et al. 2007	Spasticity Isometric strength and velocity Range of motion	Virtual reality: Hand manipulator system, Virtual environment used CAVE Automatic Virtual Environment Library
Combs et al. 2012	Speed and smoothness of movement	Motion Monitor short-range transmitter system (Innovative Sports Training, Inc., Chicago, IL) with use of “mini-bird” sensors
Thielbar et al. 2014	Range of motion	Virtual Reality and wearable sensor: actuated virtual keypad (AVK) system with Cyberglove (PneuGlove) bend sensors (2000–0201, Flexpoint Sensor Systems, Inc.) located on the dorsal side of the glove
Peters et al. 2021	Motor assessment of lower extremity	Synchronous telerehabilitation: Fugl-Meyer telerehabilitation (FM-tele)
Parkinson's disease
Westin et al. 2010	Motor assessment of upper extremity	Asynchronous: text messages
Wang et al. 2011	Motor assessment of upper extremity	Virtual reality: A projection-based VR system connected with the Patriot system was used. Open Graphics Library, Microsoft Visual and MFC
Arias et al. 2012	Motor assessment of upper extremity	Virtual reality: immersive experience a pair of Vuzix iWear VR920 glasses which includes motion tracking
Westin et al. 2012	Diagnostic and prognostic (i.e., assessment of symptoms)	Asynchronous: mobile Application
Cubo et al. 2017	Diagnostic and prognostic (i.e., assessment of symptoms)	Home-based motor monitoring: wireless motion sensor technology (KinesiaTM, Great Lakes)
Borzi et al. 2022	Postural instability	Wearable sensor: STMicroelectronics system-on-board prototype neMEMSi [27] was equipped with the following components: a 9-axis IMU (LSM9DS0)
Safarpour et al. 2022	Postural instability, Rigidity, Quality of gait	Wearable sensor: Opals by APDM Wearable Technologies, Portland, OR, USA
Multiple sclerosis
Leocani et al. 2007	Motor assessment of upper extremity	Virtual reality and sensor: Visual feedback using dedicated software (Khymeia SRL, Padova, Italy) with magneto-electric sensor, (Polhemus Inc, Colchester, VT)
Kane et al. 2008	Diagnostic and prognostic (i.e., assessment of symptoms)	Synchronous videoconferencing
Finkelstein et al. 2018	Motor assessment of upper extremity	Synchronous: telerehabilitation (MS-Home Automated Telemanagement (HAT) system))
Maillart et al. 2020	Diagnostic and prognostic (i.e., assessment of symptoms)	Asynchronous: MSCopilot mobile application
Spinal cord injury
Trincado-Alonso et al. 2014	Range of motion Diagnostic and prognostic (i.e., assessment of symptoms)	Virtual reality and wearable sensor: Virtual Reality (Toyra) and IMUs (MTx Xsens Company (Xsens Inc., Netherlands)
An et Park. 2022	Knee extension velocity	Virtual reality and wearable sensor

Activities and participation

Thirty-two studies reported on outcomes documenting changes in or status of activity limitations. No studies reported on outcomes specific to social participation restriction. Studies remotely assessed functional mobility, gait, and physical activity (n = 12), upper-extremity function including manipulation, grasp-and-release and reaching (n = 10), balance limitations (n = 7), and activities of daily living including communication activities and speech (n = 4). Virtual reality technology with or without wearable sensors or haptic systems was most frequently used, followed by wearable sensors combined or not with mobile applications and synchronous telerehabilitation (Table 2B).

Table 2B.

Remote assessments used to assess activity limitation in various neurological conditions as per the International Classification of Functioning, Disability and Health.

References	Clinical assessment targeting activity limitation	Telerehabilitation platforms
Acquired brain injury (ABI)
Theodoros et al. 2003	Speech and communication	Synchronous telerehabilitation (teleplatform)
Cuthbert 2012	Balance	Virtual reality: Wii Balance board
Traumatic brain injury (TBI) (including concussion)
Zhang et al. 2001	Activity of daily living	Virtual reality: SOFTHAVEN (LinCom, Houston, TX)
Rábago et al. 2011	Balance	Virtual reality: CAREN System
Sessoms et al. 2015	Balance	Virtual reality: CAREN System
Vargas et al. 2017	Balance	Synchronous telemedicine: Telemedicine robot (VGo; VGo Communications, Inc., Nashua, NH)
Wright et al. 2017	Balance	Virtual reality: Visual stimulation (game)
Salisbury et al. 2018	Balance	Virtual reality and wearable sensors: COTS head-mounted wearable smart glasses
Stroke
Broeren et al. 2006	UE function (i.e., manipulation, grasp, release, reaching)	Wearable sensors: Device recording all positions, movements, and forces, which are stored in the computer for further processing, analysis, and assessment.
Broeren et al. 2007	UE function (i.e., manipulation, grasp, release, reaching)	Virtual reality and haptic: Computer instrumentation haptic, force feedback and 3-dimensional visualization applied to computer games)
Sungmin et al. 2016	UE function (i.e., manipulation, grasp, release, reaching)	Virtual reality: Virtual BBT
Luque-Moreno et al. 2016	Functional mobility and gait	Virtual reality and wearable sensors: Wearable Sensors adapted Virtual Reality Rehabilitation System (VRRS), 3D motion-tracking system (Polhemus FASTRAK® 3Space, Vermont, USA)
Tobler-Ammann et al. 2016	UE function (i.e., manipulation, grasp, release, reaching)	Virtual reality and haptic: Handle instrumented with force sensors mounted on a PHANTOM Omni haptic device (Geomagic, USA)
Trinh et al. 2016	Functional and ambulatory movements- stepping, UE movements	Virtual reality: Wii Balance board
Wittmann et al. 2016	UE function (i.e., manipulation, grasp, release, reaching)	Virtual reality and wearable sensor: ArmeoSenso is a motion capture system based on wearable sensors in combination with an all-in-one touch screen computer (Inspiron 2330, Dell Inc., Fig. 1(a)).
Perez-Marcos et al. 2017	Functional mobility and gait	Virtual reality: Tabletop version of Mind-Motion™ PRO (MindMaze SA, Switzerland)
Hung et al. 2019	UE function (i.e., manipulation, grasp, release, reaching)	Virtual reality and wearable sensor: Accelerometer to ActiLife software, Kinect2Scratch Game
Thielbar et al. 2020	UE function (i.e., manipulation, grasp, release, reaching)	Virtual reality: Virtual Environment for Rehabilitative Gaming Exercises (VERGE)
de Rooij et al. 2021	Functional mobility and gait	Triaxial accelerometer (DynaPort MM, McRoberts BV, the Hague, the Netherlands)
Szturm et al. 2021	UE function (i.e., manipulation, grasp, release, reaching)	Virtual reality with sensor: Computer game-assisted tele-rehabilitation platform (GTP) with a novel application of a miniature inertial computer mouse (IB mouse)
Parkinson disease
Giansanti et al. 2008	Physical activity (step count)	Wearable sensor: Sensor embedded in a band using a force sensing resistor
Gilat et al. 2013	Functional mobility and gait	Virtual reality: system not specified
Arora et al. 2015	Functional mobility (fine motor) and gait (reaction time, gait)	Mobile application: Software developed by the research team (MAL.)
Killane et al. 2015	Functional mobility and gait (dual tasking)	Virtual reality: Virtual corridor
Horin et al. 2019	Functional mobility and gait	Wearable sensor and mobile application: wGT3X-BT Activity Monitor, Actigraph, FL, USA with a three-axis accelerometer
Lheureux et al. 2020	Functional mobility and gait	Virtual reality: Homemade environment created with Unity software (USA) and written in C# (Visual Studio, Microsoft, United States; iVR headset (HTC, Vive, Taiwan), and Inertial Measurement Units. (IMeasureU Research, VICON, United Kingdom)
Ona et al. 2020	UE function (i.e., manipulation, grasp, release, reaching)	Virtual reality: Immersive virtual reality
de Carvalho Lana et al. 2021	Physical activity (step count)	mHealth devices (Google Fit, Health, STEPZ, Pacer, and Fitbit INC®)
Hawkins et al. 2021	Balance	Immersive virtual reality standaing balance test
Multiple sclerosis
Sola-Valls et al. 2015	Physical activity (step count)	Wearable sensor and mobile application: Portable triaxial accelerometer (Actigraph GT3X)
Wall et al. 2018	Speech and communication	Mobile application: Cognitive Assessment for Aphasia App (C3A)
Spinal cord injury
Kowalczewski et al. 2011	UE function (i.e., manipulation, grasp, release, reaching)	Virtual reality: ReJoyce (Rehabilitation Joystick for Computer Exercise) automated hand function test (RAHFT)

Objective 3: Psychometric data

Out of 50 studies, 17 studies provided psychometric data for the remote outcome measures used. These included test–retest reliability of remote assessment, validity, and correlation between remote and in-person or traditional assessment as well as information around usability, acceptance, and satisfaction. A single study provided minimal clinical important difference (MCID) and normative values. Specifically, a cutoff score of steps per day was documented using wearable sensors that enabled the differentiation of ambulatory status in people living with Parkinson's disease⁶² (Table 3).

Table 3.

Available psychometric data for remote assessments of neurological conditions.

References	In-person assessment	Remote assessment/Telerehabilitation platforms	Psychometric data
Theodoros et al. 2003	Frenchay Dysarthria Assessment (FDA)	FDA/ Synchronous videoconferencing	Internal consistency reliability (ICC-95%CI): 90% level of agreement between in-person and remote FDA assessment for the overall rating of severity of dysarthria.
Kane et al. 2008	Kurztke Expanded Disability Status Scale (EDSS)	EDSS/ Synchronous videoconferencing	Interrater reliability (Cronbach's alpha): Strong correlations between in-person and remote assessor (coefficients between 0.96 and 0.99) with respect to the EDSS total score.
Arias et al. 2012	NA	Finger tapping/ Immersive virtual reality (Vuzix iWear VR920 glasses)	Test–retest reliability (ICC-95%CI): Tapping Frequency for all groups (ICC 0.94) Internal consistency reliability (ICC-95%CI): Virtual reality between in-person assessors excellent for Tapping Frequency for all groups (ICC 0.98)
Cuthbert 2012	Berg Balance Scale (BBS)	Wii score/ Virtual reality: Wii Balance Board	Validity (spearman correlation coefficient): Between Wii Balance board tilt and BBS ranged from 0.28 and 0.68 (Static balance)
Westin et al. 2012	Unified Parkinson Disease Rating Scale (UPDRS), Hauser diary	Test battery/ Mobile application	Internal consistency reliability (Cronbach's alpha): Test battery (0.85) Test–retest reliability (ICC-95%CI): For the combined group 0.88 Validity (Pearson's correlation coefficient): Between Mobile App and UPDRS (r = −0.64) Between Mobile App and Hauser diary (r = 0.35)
Arora e al. 2015	UPDRS	Remote modified UPDRS/ synchronous videoconferencing	Sensitivity: The mean sensitivity in discriminating people with PD from controls using remote UPDRS was 96.2% (SD 2%) Specificity: The mean specificity in discriminating people with PD from controls using remote UPDRS was 96.9% (SD 1.9%)
Sola-Valls et al. 2015	6 Minute Walt Test (6MWT) EDSS	6MWT and EDSS /A portable triaxial accelerometer (Actigraph GT3X) and telephone	Interrater reliability (Kappa coefficient 95% CI): Multimedia EDSS (total sample) vs in-person EDSS (0.4) Telephonic EDSS (total sample) vs in-person EDDSS (0.3) Validity (Pearson's correlation coefficient): Strong correlation between the MWT accelerometer and the in-person 6MWT (r = 0.76) Cutoff score: A cutoff of 3279.3 steps/day allowed to discriminate between patients with ambulatory impairment and fully ambulatory
Tobler-Ammann et al. 2016	Box and Blocks Test (BBT), Nine-hole Peg Test (NHPT)	Virtual Peg Insertion Test (VPIT)/ Virtual reality	Test–retest reliability (ICC-95%CI): Good to high overall test–retest reliability (ICCs ranging from 0.83 to 0.94). Fair reliability for grasping force (ICC = 0.75) and trajectory error (0.70) Poor reliability for trajectory error [cm] (0.67) and number of dropped pegs (0.58) Concurrent validity (Pearson's correlation coefficient): Low correlations between the VPIT and the NHPT (ρ = 0.31 for time; ρ = 0.21 for number of dropped pegs) Low correlations between the VPIT and the BBT (ρ = −0.23 for number of transported cubes; ρ = −0.12 for number of dropped cubes).
Sungmin et al. 2016	Box and Blocks Test (BBT)	Virtual BBT (VBBT)/ Virtual reality	Validity (Pearson's correlation coefficient): Strong correlations between the in-person BBT and virtual VBBT for nonhemiplegic (r = 0.904, p = 0.001) and hemiplegic sides (r = 0.788, p = 0.012)
Wittmann et al. 2016	Fugl-Meyer Assessment upper extremity (FMA-UE)	FMA-UE/ Virtual reality with wearable sensor (ArmeoSenso comprises a motion capture system based on wearable sensors)	Validity (Pearson's correlation coefficient): Significant correlation between the in-person FMA-UE and the automated FMA-UE with the number of workspace voxels (r = 0.91, p < 0.001), number of reached targets (r = 0.96, p < 0.001), and time to reach target (r = 0.92, p < 0.001)
Vargas et al. 2017	Standardised Assessment of Concussion (SAC), King-Devic test (K-D), Modified Balance Error Scoring System (mBESS)	SAC, mBESS, K-D/ Synchronous telemedicine robot (Vgo)	Mean difference (seconds): 100% agreement between in-person K-D and remote telemedicine K-D within 3 s differences (95% CI). 100% agreement between in-person SAC and remote telemedicine SAC (95% CI). 100% agreement between in-person sideline mBESS scores and remote mBESS score within 3 points (6/6; 95%CI 54%–100%).
Wright et al. 2017	K-D, Balance Error Scoring System (BESS), Oculomotor Tests	VR-Balance/ Virtual reality: Visual stimulation (game)	Validity (Pearson's correlation coefficient) The VR-balance test correlated most significantly with: the gaze stabilization test (rmax = 0.55) the optokinetic stimulation (rmax = 0.40) the horizontal eye saccades (rmax = 0.41) No correlation with near-point convergence distance, King-Devick total time, dynamic visual acuity test, and total BESS score
Wall et al. 2018	Cognitive Assessment for Aphasia Paper-test	Cognitive Assessment for Aphasia App (C3A)/ Mobile application	Feasibility and acceptability Administration time for the C3A was approximately 20 min. No statistical difference (p = 0.38) in participant preferring C3A over paper-test between the aphasia (71%), stroke nonaphasia (76%) and control groups (59%), respectively.
Maillart et al. 2020	Multiple Sclerosis Functional Composite (MSFC)	MSFC APP/ Mobile application (MSCopilot)	Test–retest reliability (ICC-95%CI) Good reproducibility of MSCopilot (ICC = 0.90; p < 0.001) Validity (Pearson's correlation coefficient) Strong correlation between the digital and in-person MSFC test scores (r = 0.81; p < 0.001) Satisfaction The majority of patients preferred the use of the mobile application over traditional tests.
Ona et al. 2020	Box and Blocks Test (BBT) Hoehn and Yarh scale	BBT/ Immersive virtual reality	Test–retest reliability (ICC-95% CI) Excellent correlation between the two attempts for the most affected side (ICC = 0.876) and for the less affected side (ICC = 0.873) Validity (Pearson's correlation coefficient) Statistical significance and moderate correlation between BBT and VR-BBT for more affected side (p = 0.025, r = 0.499) and less affected side (p = 0.022, r = 0.510) Statistical significance and moderate correlation between Hoehn and Yahr scale and the VR-BBT for: more affected side (p = 0.001, r = 0.496) and less affected side (p = 0.038, r = 0.696) Usability and acceptability High to excellent perceived clinician usability and acceptability of the VR-BBT
de Carvalho Lana et al. 2021	2 Minute Walk Test (2MWT)	Step count/ mHealth devices (Google Fit, Health, STEPZ, Pacer, and Fitbit INC®)	Validity (Pearson's correlation coefficient) GoogleFit and STEZ were very highly correlated to 2MWT (r = 0.92, r = 0.91) Pacer and Fitbit INC® were highly correlated to 2MWT (r = 0.77, r = 0.82) Health was moderately correlated to 2MWT (r = 0.54)
Safarpour et al. 2022	MDS-revised Unified Parkinson's Disease Rating Scale (MDS-UPDRS)	Walking hours, gait speed and sway area/ wearable sensors Opals	Validity (Pearson's correlation coefficient) Statistically significant correlation between the in-person MDS-UPDRS PIGD subscore and the remote sensors values of gait speed, gait time combined with postural sway (r = 0.61)

Discussion

The majority of studies identified for this review are feasibility or pilot studies (level V evidence), reflecting the novelty and preliminary exploration of the use of certain technologies and virtual care systems for remote assessment of PwN.

Remote outcome measures

Studies reported on lab-based or expensive remote technology including haptic systems,^18,23 Computer-Assisted Rehabilitation Environment system (CAREN),^55,56 Actigraph,^40,62 and immersive virtual reality.^{28,41,42,44,48} Results from this review also revealed that asynchronous technology (i.e., mHealth or wearable sensors using store-and-forward communication) was used more frequently than synchronous technology (i.e., videoconference) when assessing PwN. Importantly, this scoping review demonstrates that there is a significant gap in the determination of needed psychometric characteristics of remote assessment developed for people living with neurological conditions. Telerehabilitation is growing and is quicky being accepted as a way to reduce access barriers for people living with neurological conditions.^68–72 The use of sensor technology including wearables, wireless motion sensors, and smart home technology is expanding, in fact, some authors have even identified remote assessment as its own field in telemedicine called “telesemiotic.”⁷³ For instance, Cubo et al. used motion sensor to capture changes in motor symptoms for people living with Parkinson's³⁹ while others have studied the use of home sensors to assess safety and night-time wandering with people living with dementia.⁷⁴ Usability of a device is critical for self-monitoring, autonomous use, and self-efficacy.^75,76 Emerging smart home technologies have the potential to improve independence, quality of life, and remote monitoring for people with disability.^77,78 Access, cost, portability, and availability of teleplatforms must be considered as laboratory-grade devices or expensive remote assessments may pose a challenge for clinical adoption and implementation.

Types of assessment as per the International Classification of Functioning, Disability and Health

Studies incorporating remote assessments of body impairments were mostly completed with people living with impairments from stroke, multiple sclerosis, and Parkinson's disease. Mobile applications and wearable sensors were most frequently used and were reported as easy to use. Specifically, people living with Parkinson's disease reported that mHealth applications were easy to use.⁴⁰ The use of remote supervision to monitor changes in symptoms and inform prognosis is increasingly reported when monitoring symptoms of Parkinson's disease^36,39,46,47 and could well be suited for other neurodegenerative conditions such as multiple sclerosis, amyotrophic lateral sclerosis, and muscular dystrophy.

Studies of activity and participation used wearable technologies to detect changes in activities such as mobility, gait, and upper extremity functional tasks. This supports findings from recent systematic reviews reporting on the use of mobile devices to assess gait in people with Parkinson's disease,⁷⁹ to assess physical activity and mobility in PwN,⁷ and on the feasibility of using remote assessments for hand function in PwN.⁸⁰ To date, a limited number of studies have targeted the use of remote assessment for social participation or community reintegration outcomes for PwN.^81,82 Community integration and societal participation are crucial components of life satisfaction for PwN.⁸³ Future research should, therefore, focus on the development of remote assessments targeting social participation for PwN. Defining the ICF construct being assessed in terms of body impairments, activity limitation, or participation restriction may facilitate the development and clinical use of remote assessment.

Psychometric data

Only one-third (n = 17) of the studies documented the psychometric properties of the remote outcome measures used. Importantly, these psychometric properties were mostly established in a controlled and standardized environment such as a laboratory setting. Therefore, it is critical to determine similar properties in clinical and home-based environments. No studies reported the standard error of measurement, the data around floor and ceiling effects as well as measure responsiveness is lacking and only one study documented the MCID and clinical normative data, making it harder for clinicians to rely on remote assessments for treatment planning.

Moderate to strong correlations between in-person and remote assessment measures were reported in a small number of the studies. However, a lack of congruence between the assessments performed in-person and remotely was also frequently documented. For example, Tobler-Ammann et al.²³ documented high test–retest reliability for the virtual version of the Nine Hole Peg Test (NHPT) but when they compared it to an in-person assessment with a similar construct such as the Box & Block Test (BBT), correlations were low for the number of transported cubes (p = −0.23) and dropped cubes (p = −0.12). It is unclear if this difference was observed due to a lack of correlation between the in-person BBT and the virtual NHPT or because one test was administered in-person and one was administered virtually. Similarly, Wang et al. reported that people living with Parkinson's disease reached less far when measured using a wearable sensor in a virtual environment compared to the in-person environment while completing the same task.⁴⁹ Although the same measure could be administered in a virtual and in-person context, they may be measuring different constructs as described by the ICF. For example, the in-person BBT could be measuring an activity-related task, while the virtual reality version could be measuring body impairments such as the velocity of finger movements. Since psychometric properties may differ depending on the context within which the assessment is being administered, validity, reliability, diagnostic accuracy, and normative values associated with a clinical measure should be established independently for both in-person and remote assessments⁸⁴ and within the context that it is being used.⁸⁵ Identifying comparative psychometric norms between in-person and remote assessments is key to the successful and effective hybrid use of telerehabilitation. It is also critical that validated remote assessments specific to other neurological injuries not included in this review be made available.

Limitations and opportunities

The terminology used as search terms, as well as by authors to describe remote assessment, is an important limitation to note as it might have limited the number of studies identified and may have excluded potentially eligible studies for this scoping review. For example, overarching terms for telehealth, mHealth and telemedicine were used as search terms in this review, which may have led to missing key studies which used specific terms or devices for telehealth assessments such as smartphone applications. Future studies should expand search terms to include specific technologies used in telehealth. Our initial search was completed in 2020 near the beginning of the COVID-19 pandemic and an updated search was performed in May 2022 (2 years into the pandemic). Due to the rapidly growing body of literature on this topic, future updates will be necessary.

Conclusion

It is critical for healthcare providers to consider the neurological condition, the outcome being assessed as per the ICF, the type of remote monitoring, as well as teleplatform used when using remote assessment clinically. Results from our review demonstrated that the identification and reporting of psychometric data for remote clinical outcome measures is still novel and psychometric properties may differ when being assessed in clinical or home environments, and laboratory spaces. When possible, clinicians should use remote assessments which have been validated in a clinical or home space before adopting them within their clinical practice. It is necessary to establish the reliability and validity of remote assessments for each neurological condition to improve adoption and clinical implementation of assessment measures within various clinical contexts.

Footnotes

Authors’ note

International Registered Report Identifier (IRRID): PRR1-10.2196/27186.

Acknowledgments

The authors acknowledge the valuable assistance from the UOttawa librarian in the development of the search strategy.

Contributorship

Each author has made substantial contribution to the development of the methodology, data collection and analysis, and interpretation of the findings. Each author contributed to the redaction, and revision of this manuscript.

Declaration of conflicting interests

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

Ethical approval

Not applicable for this scoping review.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: KB is supported by an admission scholarship from the University of Ottawa and by a grant provided by the Workplace Safety and Insurance Board (Ontario). The provision of grant support by WSIB does not in any way infer or imply endorsement of the content by the WSIB.

Guarantor

Not applicable

ORCID iD

Jennifer O’Neil

Appendix 1

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Identification and description of telerehabilitation assessments for individuals with neurological conditions: A scoping review

Abstract

Background

Methods

Results

Conclusion

Keywords

Introduction

Methods

Systematic search

Data extraction

Results

Included studies

Objective 1: Identification of the neurological conditions assessed and the characteristics of the clinicians

Objective 2: Remote outcome measures described using the International Classification of Functioning, Disability and Health

Body structures

Activities and participation

Objective 3: Psychometric data

Discussion

Remote outcome measures

Types of assessment as per the International Classification of Functioning, Disability and Health

Psychometric data

Limitations and opportunities

Conclusion

Footnotes

Authors’ note

Acknowledgments

Contributorship

Declaration of conflicting interests

Ethical approval

Funding

Guarantor

ORCID iD

Appendix 1

References