Published on in Vol 12 (2024)

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Mobile and Computer-Based Applications for Rehabilitation Monitoring and Self-Management After Knee Arthroplasty: Scoping Review

Mobile and Computer-Based Applications for Rehabilitation Monitoring and Self-Management After Knee Arthroplasty: Scoping Review

Mobile and Computer-Based Applications for Rehabilitation Monitoring and Self-Management After Knee Arthroplasty: Scoping Review


1Department of Research & Development, The George Institute for Global Health India, Delhi, India

2Department of Orthopaedics, All India Institute of Medical Sciences, Delhi, India

3Department of School of Exercise & Nutrition, Institute for Physical Activity and Nutrition, Deakin University, Geelong, Australia

Corresponding Author:

Niveditha Devasenapathy, MBBS, PhD

Department of Research & Development

The George Institute for Global Health India

308, Third floor, Elegance Tower

Plot no 8, Jasola District Centre

Delhi, 110025


Phone: 91 11 4158 8091 93


Background: Successful post-knee replacement rehabilitation requires adequate access to health information, social support, and periodic monitoring by a health professional. Mobile health (mHealth) and computer-based technologies are used for rehabilitation and remote monitoring. The extent of technology use and its function in post-knee replacement rehabilitation care in low and middle-income settings are unknown.

Objective: To inform future mHealth intervention development, we conducted a scoping review to map the features and functionality of existing technologies and determine users’ perspectives on telerehabilitation and technology for self-management.

Methods: We followed the Joanna Briggs Institute methodology for scoping reviews. We searched the Embase, Medline, PsycINFO via OVID, and Cochrane Central Register of Controlled Trials databases for manuscripts published from 2001 onward. We included original research articles reporting the use of mobile or computer-based technologies by patients, health care providers, researchers, or family members. Studies were divided into the following 3 categories based on the purpose: validation studies, clinical evaluation, and end user feedback. We extracted general information on study design, technology features, proposed function, and perspectives of health care providers and patients. The protocol for this review is accessible in the Open Science Framework.

Results: Of the 5960 articles, 158 that reported from high-income settings contributed to the qualitative summary (64 studies on mHealth or telerehabilitation programs, 28 validation studies, 38 studies describing users’ perceptions). The highest numbers of studies were from Europe or the United Kingdom and North America regarding the use of a mobile app with or without wearables and reported mainly in the last decade. No studies were from low and middle-income settings. The primary functions of technology for remote rehabilitation were education to aid recovery and enable regular, appropriate exercises; monitoring progress of pain (n=19), activity (n=20), and exercise adherence (n=30); 1 or 2-way communication with health care professionals to facilitate the continuum of care (n=51); and goal setting (n=23). Assessment of range of motion (n=16) and gait analysis (n=10) were the commonly validated technologies developed to incorporate into a future rehabilitation program. Few studies (n=14) reported end user involvement during the development stage. We summarized the reasons for satisfaction and dissatisfaction among users across various technologies.

Conclusions: Several existing mobile and computer-based technologies facilitate post-knee replacement rehabilitation care for patients and health care providers. However, they are limited to high-income settings and may not be extrapolated to low-income settings. A systematic needs assessment of patients undergoing knee replacement and health care providers involved in rehabilitation, involving end users at all stages of development and evaluation, with clear reporting of the development and clinical evaluation can make post-knee replacement rehabilitation care in resource-poor settings accessible and cost-effective.

JMIR Mhealth Uhealth 2024;12:e47843



Knee arthroplasty is the gold standard treatment for end-stage osteoarthritis when conservative treatments fail to relieve symptoms [1]. Wound care and postarthroplasty physiotherapy are essential components of this treatment. Poor adherence to physiotherapy could delay the recovery and lead to suboptimal functional outcomes [2]. Beyond in-hospital clinical care and initiation of physical therapy before discharge, continued and reliable access to information, support from health care providers, awareness of the recovery pathway, easy access to rehabilitation centers, and periodic monitoring are influential factors for optimal recovery [3-6]. In addition to an uneventful surgery, postarthroplasty outcomes are associated with several patient-related factors such as their preoperative physical and mental state, comorbidities, social support, and socioeconomic status, emphasizing the need for personalized approaches [7]. Hence, monitoring of the rehabilitation phase is essential, whether at clinics, in rehabilitation units, or at home [8-10].

Technology-assisted remote monitoring methods are increasingly being advocated in high-income countries. There is low to moderate-quality evidence on the superiority of telerehabilitation compared with unsupported home-based rehabilitation and noninferiority compared with clinic-based monitoring with respect to range of motion (ROM), pain, function, quality of life, and cost-effectiveness at 3 months between clinic-based and home-based rehabilitation strategies using technology [11-17]. Hence, current evidence supports the adaptation of technology-based rehabilitation as feasible, as safe, and as good as clinic-based monitoring with an additional benefit of saving out-of-pocket expenditure. Technology-based approaches are diverse, varying from telehealth [17] to virtual reality techniques [13] aimed at improving adherence to physical therapy and facilitating remote monitoring [12] of patient progress during the post-acute rehabilitation phase [18].

Therefore, the aim of this scoping review was to summarize the extent, range, and nature of technology used for provision of rehabilitation or to monitor progress following knee arthroplasty. This scoping review aimed to address the following objectives:

  1. To map the characteristic features and functionality of the technologies, guiding or theoretical framework for designing the technology, and evaluation methodologies of mobile technology–based apps for rehabilitation monitoring and self-management following knee arthroplasty
  2. To understand the patient and physical therapist perspectives regarding the use of mobile technology–based apps for rehabilitation monitoring and self-management following knee arthroplasty

To our knowledge, there are no existing scoping reviews that address our aims [19]. The information from this review will help us and other researchers make an informed decision on future mobile health (mHealth) interventions for monitoring post-knee arthroplasty rehabilitation care by physiotherapists and orthopedic surgeons and to promote self-management by individuals. This review will also help highlight existing gaps in the context of low and middle-income countries (LMICs).

We conducted this scoping review following the Joanna Briggs Institute (JBI) methodology for JBI Scoping Reviews [20] and consulted the PRISMA-ScR (Preferred Reporting Items for Systematic Review and Meta-Analyses Extension for Scoping Reviews) checklist for reporting [21]. The protocol was registered at the Open Science Framework [22].

Data Sources and Searches

To identify relevant studies, an electronic database literature search was conducted in the Embase, Medline, PsycINFO via OVID, and Cochrane Central Register of Controlled Trials (CENTRAL) databases using the following key terms: “Knee arthroplasty OR Knee replacement,” “mobile,” “web,” “remote sensor,” “computer,” “telerehabilitation,” and “m-health” (Tables S1 and S2 in Multimedia Appendix 1). The search was executed in October 2021 and updated in August 2023. The search was restricted to 2001 onward. There were no language restrictions during the search. We searched the reference list of included articles to identify potentially eligible studies.

Study Selection

Predefined inclusion criteria were articles reporting the use of mobile or computer apps or any other technologies such as sensor-based devices for delivering or monitoring rehabilitation either scheduled or following knee joint replacement. We also included proof- of-concept papers that described the development process of a mobile or technology-based solution for rehabilitation. The purpose of technology could be for a health care provider to monitor rehabilitation adherence, to aid patient-health care provider communication, to promote self-management, to act as reminders, or to act as a source of education or any other function that is aimed at rehabilitation care following knee replacement. The app or technology could be used by patients, health care providers, researchers, or a family member. Included studies could have been conducted in the community or home for any clinical setting in any geographic region. The studies were required to be original research articles, and we included experimental and observational studies using quantitative or qualitative research methods. Reviews (narrative or systematic reviews), non-English articles, and articles without abstracts or full texts were excluded.

Data Extraction

Screening of manuscript titles and abstracts was conducted by 2 independent reviewers using the web app Rayyan [23]. Prior to screening, reviewers discussed inclusion and exclusion criteria to ensure consistency between individuals. Two reviewers assessed the eligibility of the full text, and disagreements were resolved by discussion. Systematic reviews were not included in the review but were used to obtain potentially relevant references. Multiple publications originating from a single technology were grouped and presented as 1 study.

For data charting purposes, the studies were divided into the following 3 categories: (1) studies that had no rehabilitation program but included an app or a technology to assess ROM or gait and were validation studies, (2) studies reporting the use of a mobile or computer app or a telehealth delivery platform for a rehabilitation program with or without sensor-based devices and wearable sensors, (3) studies that reported end users’ perceptions of the technology used for rehabilitation monitoring. Data on the general information for the studies, features of the technology, the proposed function, and perspectives of health care providers and patients were extracted and entered in Microsoft Excel. If only the protocol of a planned study was available, there was no information on clinical evaluation, or the study included <6 individuals, we did not extract data beyond the general information.

Search Results

The database search, including the ad hoc search, yielded 5960 articles. Of these articles, 300 articles were considered potentially relevant. Of these, 158 articles were included for data extraction, 131 articles were excluded, and 11 articles were not available (Table S3 in Multimedia Appendix 1). Of the 158 articles, 91 articles (64 studies) reported the clinical evaluation of a technology-based rehabilitation program, 29 articles (28 studies) reported the validation or a proof of concept of technology intended to be used for rehabilitation, and 13 articles were protocols of evaluation studies. In addition, 25 articles reported end users’ perceptions on technology (Figure 1) as stand-alone articles or part of clinical evaluation studies (n=13), totaling 38 studies. The 13 studies that reported the perceptions of technology that were also included in rehabilitation program studies were removed from the final list of included full-text articles to avoid double counting.

Figure 1. Process of identifying and including studies according to PRISMA-ScR (Preferred Reporting Items for Systematic Review and Meta-Analyses Extension for Scoping Reviews).

Technology for Rehabilitation

Characteristics of the 105 Studies

Studies were reported from Europe and the United Kingdom (n=45) [11, 24-60, 62-66, 169, 170], North America (n=34) [6, 67-99], Australia and New Zealand (n=10) [100-109], and Asia (n=16) [110-125]. None of the studies were from LMICs. Reports of mobile-based technologies represented the highest number (54/105, 51.4%) [6, 25-27, 31, 32, 36, 37, 41-50, 52, 55, 56, 58-60, 62, 64, 67-69, 74, 75, 78, 80, 82, 83, 90-92, 95, 101, 103, 106-108, 112, 113, 117, 121-126, 169], followed by computer applications (31/105, 29.5%) [24, 29, 30, 33-35, 39, 53, 54, 57, 65, 66, 70, 73, 76, 79, 84-86, 89, 93, 97, 98, 100, 102, 104, 111, 114, 116, 120, 127], and tele/video/web conferencing (20/105, 19%) for rehabilitation monitoring [28, 38, 51, 63, 71, 72, 77, 81, 87, 88, 94, 96, 99, 105, 109, 110, 115, 118, 119, 170]. The highest use of mobile apps associated with or without a wearable was in Europe and the United Kingdom, followed by North America. Tele/video/web conferencing was used across regions, with the highest number in North America (Figure 2).

Figure 2. Number of studies published by region based on different technologies (n=105).
Validation Studies

There were 28 validation studies. Studies that validated stand-alone technologies included those to assess ROM (n=16) [24, 29, 43, 45, 48, 49, 52, 68, 73, 84, 100, 101, 111, 112, 116, 125] or gait or posture (n=10) [29, 30, 33, 53, 57, 74, 89, 97, 102, 124], and 2 studies involved technologies to monitor exercises [98, 114]. The technologies involved were either wearables (n=20) [24, 29, 30, 33, 45, 48, 49, 57, 68, 73, 84, 89, 97, 98, 100, 111, 112, 114, 116, 125], sensor-based devices (nonwearables; n=4) [53, 66, 102, 124], or inbuilt sensors available within a smartphone (n=4) [43, 52, 74, 101] (Table S4 in Multimedia Appendix 1).

In terms of study design, 9 were cross-sectional studies [33, 48, 52, 57, 84, 89, 97, 101, 116], 7 were cohort or longitudinal studies [45, 53, 68, 74, 100, 111, 125], 5 were pre-post studies [29, 30, 43, 73, 102], 1 was an uncontrolled trial [112], 1 was a randomized controlled trial (RCT) [66], and 5 were articles that described the proof of concept or development plan for the technologies [24, 49, 98, 114, 124]. The participant sample size ranged from 1 to 60. Most studies reported reliability between a standard or universal goniometer and smartphone app goniometry and the clinical evaluation of sensors to measure gait parameters (Table S4 in Multimedia Appendix 1). In 7 studies, gait was measured using sensors provided by a health care provider in a hospital setting [29, 33, 57, 74, 89, 97, 102], and 3 studies did not describe the measurement setting [30, 53, 124].

Clinical Evaluation Studies

There were 64 clinical evaluation studies. The technology consisted of a mobile or computer app with a wearable device (n=18) [6, 26, 31, 32, 39, 44, 46, 50, 54, 64, 67, 69, 90, 92, 95, 106, 108, 169], a mobile or computer app with a sensor-based device (n=13) [25, 34, 35, 40, 42, 65, 70, 76, 79, 85, 86, 93, 120], only a mobile app (n=14) [36, 37, 55, 56, 62, 75, 78, 80, 83, 107, 113, 117, 123, 128], or only telephone or videoconferencing (n=19) for remote monitoring [28, 38, 51, 63, 71, 72, 77, 81, 88, 94, 96, 99, 105, 109, 110, 115, 118, 119, 170]. Of the studies that used a mobile app, 9 studies were developed only for iOS [55, 67, 69, 71, 77, 92, 106, 107, 109], 1 was an Android app [42], 7 were for both Android and iOS devices [28, 36, 56, 88, 108, 115, 117], and 21 studies did not specify the platform (Multimedia Appendix 2). A web-based clinician portal for synchronous or asynchronous remote monitoring of patients was reported by 36 studies (Table 1). The number of published studies and the intervention arm sample size (ranging from 7 to 2292), especially for those that included wearable sensors and mobile apps, steadily increased over the last 2 decades (Figure 3).

Table 1. Summary of application functionality (N=64).
First author, yearWeb portalDevicesPeerApp name

MonitoringWearablesSensor-based devices

Alexander, 2023 [67]Apple Watchamymobility
An, 2021 [110]
Argent, 2019 [169]IMUbAvatar
Bäcker, 2021 [25]GenuSportGenuSport
Bade, 2020 [166]In-shoe sensors
Bell, 2020 [90]InterACTION IMU
Bini, 2017 [71]Capture proof
Blasco, 2022 [28]WeChat app
Campbell, 2019 [72]StreaMD
Chughtai, 2018 [76]VERAcVERA
Chughtai, 2019 [75]PReHab
Colomina, 2021 [31]Fitbit Flex 2
Correia, 2019 [32]IMU
De Berardinis, 2022 [26]Magnetic sensors with Velcro bandskari
Doiron-Cadrin, 2020 [77]Reacts Lite
Duong, 2023 [106]Fitbit, ActivPal, Goniometer Pro
Eichler, 2019 [34]Kinect sensorMainReha app
Eisermann, 2004 [39]Accelerometers, wrist band, chest sensorsWeb cams
Farr-Wharton, 2020 [108]Garmin Vivosmart heart rate activity tracker
Fung, 2012 [79]Wii sensor balance
Gianola, 2020 [35]Avatar
Gohir, 2021 [36]i-Beat app
Gray, 2022 [37]Digital Joint School using GoWell health program
Gunduz, 2021 [38]
Hadamus, 2022 [40]Kinetic camera
Hardwick-Morris, 2022 [107]Physitrack
Hong, 2022 [80]Digital Musculoskeletal Surgical Care Program app
Huang, 2017 [113]Yishu
Janhunen, 2023 [42]Kinect sensor with TV and tablet
Juhl, 2016 [44]IMUICURA app
Klement, 2019 [81]
Knapp, 2021 [83]
Kramer, 2003 [99]
Kuether, 2019 [85]VERA
Lam, 2016 [86]IMUReHab system
Lebleu, 2023 [46]Activity tracker Garmin vívofit 4moveUP Therapy
LeBrun, 2022 [78]MyChart app
Li, 2023 [115]
Lu, 2021 [117]
McDonall, 2022 [147]
Mehta, 2020 [6]Activity tracker
Milliren, 2022 [88]Ubicare Smart X
Nuevo, 2023 [50]Accelerometer, gyroscope, magnetometer (DyCare)ReHub
Osterloh, 2023 [51]YOLii
Park, 2017 [118]
Park, 2023 [119]
Piqueras, 2013 [54](WAGYRO)Avatar
Pournajaf, 2022 [65]
Pronk, 2020 [55]Pain coach app
Prvu Bettger, 2019 [70]VERA
Ramkumar, 2019 [92]Motion sensorsFocus ventures RPM
Russell, 2011 [105]
Scheper, 2019 [56]Woundcare app
Su, 2015 [120]Kinect sensor
Summers, 2023 [93]Electro-mechanical device
Szöts, 2016 [170]
Timmers, 2019 [62]The Patient Journey app
Torpil, 2022 [63]
Tousignant, 2011 [94]
Tripuraneni, 2021 [95]Smart watch
van Dijk-Huisman, 2020 [64]MOX activity monitor
Visperas, 2021 [96]
Wang, 2023 [121]WeChat app
Zhang, 2021 [123]WeChat app

aNot applicable.

bIMU: inertial motion unit.

cVERA: Virtual Exercise Rehabilitation Assistant.

Figure 3. Technologies developed over the years by sample size (n=64), with the size of the bubble indicating the sample size of the intervention arm of all the studies published that particular year per technology category. Mobile app = mobile app + mobile app with inbuilt sensors; sensor-based device = sensor-based device with a mobile app + sensor-based device with a computer application; wearable sensors = wearable sensors with a mobile app + wearable sensors with a computer application + wearable sensors.

Although most studies described the features and functionality of the technology to deliver the intervention, they lacked details about the technological aspects that could benefit future researchers. For example, 2 studies [31, 86] explicitly reported information on the software, programming language and tools used, or calibration procedures either along with the main study or cited the article that described the development phase. Information on conceptualization of the technology-based intervention was described in only 3 studies [37, 38, 120]. End users’ involvement was typically late during the development phase (ie, prototype stage) and involved refining the functionalities and features of technology [32, 51, 64, 65, 75-77, 80, 86, 88, 115, 118, 169] prior to deployment. Patient feedback on their needs at an early development phase was reported only by Blasco et al [28].

Clinical effectiveness was tested using an RCT design in 57.8% (37/64) of the studies [6, 25, 28, 34-36, 39, 42, 44, 50, 51, 54, 55, 62, 63, 65, 67, 70-72, 77, 79, 90, 94-96, 99, 105, 106, 108-110, 117-119, 128, 170], and the rest of the studies were either retrospective comparative cohort studies (n=3) [26, 37, 78], uncontrolled cohort studies (n=9) [46, 56, 69, 76, 81, 83, 85, 92, 169], cross-sectional studies (n=1) [86], or non-RCTs (n=14) [31, 32, 38, 40, 64, 75, 80, 88, 93, 107, 113, 115, 120, 123].

We found 13 study protocols, of which 12 were RCTs published between 2013 and 2023 [11, 41, 47, 58-60, 82, 87, 91, 103, 104, 122], for which we could not find a published report and hence were not included in this summary. User experience was measured in trials using quantitative (n=9) [32, 34, 38, 50, 55, 94, 96, 105, 123], qualitative (n=2) [61, 128], and mixed methods (n=3) [39, 90, 109] approaches.

Application Functionality for Rehabilitation Programs

The key functionalities of the telerehabilitation technologies extracted from 64 studies are summarized under 4 themes, namely education and enablement, monitoring progress, communication, and goal setting (Table 2).

Table 2. Themes of the key functionalities of the telerehabilitation technologies.
First author, yearExerciseMonitoring progressFunctionsCommunication

RepositoryDiaryTracker or reminderBiofeedbackVRaFeedback to patientPainROMbKnee functionPhysical activitySedentary timeSleepTriggersGoal settingDirectionMode

Alexander, 2023 [67]cSPd2-wayText, F2Fe
An, 2021 [110]SP2-wayVideo
Argent, 2019 [169]SAfExercise2-wayF2F
Bäcker, 2021 [25]SAExercise
Bade, 2020 [166]SA2-wayF2F
Bell, 2020 [90]SA, APg2-wayVideo
Bini, 2017 [71]AP2-wayText, video, F2F
Blasco, 2022 [28]SCh1-way, 2-wayText, audio, F2F
Campbell, 2019 [72]AP1-way (SMS text messaging bot)Video, text
Chughtai, 2018 [76]SA, SP2-wayVideo
Chughtai, 2019 [75]
Colomina, 2021 [31]SA, APSCExercise2-wayText
Correia, 2019 [32]SA, AP2-wayAudio, F2F
De Berardinis, 2022 [26]SASCExercise2-wayF2F
Doiron-Cadrin, 2019 [77]SP2-wayVideo
Duong, 2023 [106]AAi, APSCActivity1-way, 2-wayText, video
Eichler, 2019 [34]SA, APExercise1-way, 2-wayAudio, video, text, F2F
Eisermann, 2004 [39]SA, AP2-wayText
Farr-Wharton, 2020 [108]AA, APDSjFunction1-wayText, audio
Fung, 2012 [79]SALower extremity function2-wayF2F
Gianola, 2020 [35]SAExercise
Gohir, 2021 [36]AA, APExercise1-way, 2-wayText, audio (tele)
Gray, 2022 [37]SP1-way, 2-wayText
Gunduz, 2021 [38]
Hadamus, 2022 [40]SA, SPExercise2-wayF2F
Hardwick-Morris, 2022 [107]SPSC2-wayVideo, text
Hong, 2022 [80]SPRecovery goals2-wayVideo
Huang, 2017 [113]
Janhunen, 2023 [42]SAExercise
Juhl, 2016 [44]SPSC2-wayUnclear
Klement, 2019 [81]1-way, 2-wayText, videos, F2F
Knapp, 2021 [83]NUk
Kramer, 2003 [99]SC2-wayAudio
Kuether, 2019 [85]SA, SP2-wayF2F, video
Lam, 2016 [86]SA, SPROM, strength
Lebleu, 2023 [46]SA, APDS2-wayText
LeBrun, 2022 [78]SPSC2-wayAudio, video
Li, 2023 [115]SP2-wayVideo, text
Lu, 2021 [117]SPSC2-wayVideo
McDonall, 2022 [147]Pain management, knee function, avoiding complications
Mehta, 2020 [6]AADS, NUActivity1-way, 2-wayText, F2F
Milliren, 2022 [88]Discharge goal1-wayText (automatic)
Nuevo, 2023 [50]SADS, NU2-wayVideo, text
Osterloh, 2023 [51]SPSC2-wayVideo
Park, 2017 [118]SC1-way, 2-wayText, audio (tele),
Park, 2023 [119]SPSC2-wayAudio calls
Piqueras, 2013 [54]SA, AP2-wayAudio (tele)
Pournajaf, 2022 [65]SAVR-based balance boardExerciseExercise
Pronk, 2020 [55]Unclear
Prvu Bettger, 2019 [70]SA, SP, APExercise2-wayVideo, F2F
Ramkumar, 2019 [92]SADSExercise1-wayText
Russell, 2011 [105]SPSCUnclear2-wayVideo
Scheper, 2019 [56]DS1-wayText
Su, 2015 [120]SAExercise
Summers, 2023 [93]SA, SPDS2-wayVideo
Szöts, 2016 [170]2-wayAudio (tele)
Timmers, 2019 [62]1-wayAudio, video, text
Torpil, 2022 [63]SCOccupation related2-wayVideo
Tousignant, 2011 [94]SPSC2-wayVideo
Tripuraneni, 2021 [95]AA1-wayText
van Dijk-Huisman, 2020 [64]SA, APSC2-wayVideo
Visperas, 2021 [96]APDS, SC1-way, 2-wayText, audio (telephone)
Wang, 2023 [121]APTask2-wayText
Zhang, 2021 [123]2-wayAudio, text, video

aVR: virtual reality.

bROM: range of motion.

cNot applicable.

dSP: synchronous from physiotherapist.

eF2F: face to face.

fSA: synchronous from app.

gAP: asynchronous from physiotherapist.

hSC: scheduled call.

iAA: asynchronous from app.

jDS: danger signs.

kNU: non-use.

Education and Enablement

An exercise repository in the form of videos, text, or infographics was one of the main features in the studies (n=53), of which only 20 studies described the list of exercises (Table S5 in Multimedia Appendix 1). Education for patients was part of the rehabilitation program in 17 studies. Table S6 in Multimedia Appendix 1 lists the topic areas covered in the education materials. Regarding exercise, 6 studies reported using an e-diary for maintaining an exercise log, 11 studies reported using reminders to perform exercises, and 13 studies reported using a tracker for exercise adherence (Multimedia Appendix 2). Feedback on the appropriateness of exercise performance was synchronous (biofeedback or virtual reality) from the app (n=19), directly from the health care provider via a video call with the patients (patient performing exercise live, measurement of ROM during video call, transmission of virtual avatar data to health care provider; n=14), or provided via both (n=6; Table 2). Feedback to the patient, which was either in the form of push notifications or a progress summary, was asynchronous from the app using automated programs in 2 studies. Asynchronous feedback from a health care provider in the form of instructions, messages, or an exercise regimen was reported in 13 studies. Feedback via both the app and a health care provider was provided in 3 studies (Table 2). Only 7 studies [6, 51, 75, 115, 117, 123, 128] had an option for peer support for patients.

Measuring Progress

Measurement of patient-reported outcomes such as pain (n=19) was an inbuilt feature in the app. Changes in knee function and activity were monitored directly via wearables or captured using patient-reported outcome measures. These included ROM in 15 studies, knee function in 8 studies, physical activity in 20 studies, sedentary behavior in 5 studies, and sleep in 4 studies. Automatic alerts were provided to the health care provider for any danger signs such as knee pain, wound health, opioid consumption, function, ROM, number of steps, exercise adherence, and any negative response to questions after entering the postoperative follow-up in 9 studies; for non-use of the technology by patients in 4 studies; and for scheduled consultations in 18 studies (Table 2).


Mobile app–enabled 1-way communication included push messages, notifications, reminders, patients’ replies to inbuilt questions in the app, information sent to the patient by the health care team, and an SMS text messaging bot (n=10). Two-way communication, either via an app or in face-to-face visits, was reported in 41 studies. In addition, 11 studies reported a combination of both 1 and 2-way communication, and 1 study did not provide sufficient information about communication. Electronic communication was delivered in the form of text, audio or video messages, and direct communication (Table 2).

Goal Setting

Goal setting for exercises, activity, pain management, knee function, ROM, muscle strength, rehabilitation, and discharge as part of the rehabilitation program was reported in 23 studies. The goals were set by either the health care provider or the patient (Table 2).

End Users’ Perceptions

Of the 38 studies that reported user perspectives, 2 focused on the perspectives of health care providers, 27 focused on the perspectives of patients and caregivers, and 9 focused on the perspectives of both groups (health care providers and patients and caregivers). The approach for data collection was quantitative (n=23), qualitative (n=9), or mixed methods (n=6). The sample size ranged from 2 to 200 health care providers and from 5 to 2292 patients (Tables S7 and S8 in Multimedia Appendix 1).

Commonly used quantitative questionnaires to assess satisfaction were the System Usability Scale [129] and the net promoter score [130]. To ratify the experience with telerehabilitation, the Telemedicine Perception Questionnaire was used [131]. Acceptability and usability were assessed using the acceptance of information technology questionnaire [132] and the Telemedicine Usability Questionnaire [133]. Some studies used bespoke questionnaires to report user experience and satisfaction [32, 39, 61, 90, 94, 105, 109, 134-146].

Overall, health care providers perceived telerehabilitation and the use of technology such as biosensors as a way of improving efficiency in providing care [146], patient adherence to exercises [39, 136, 146], patient-physician communication [136], and case management [137, 146]. The main factors associated with user satisfaction with e-consultations were reliable technology, good voice or image synchronization, the refresh rate of images, sound quality, and operability of the peripherals [94, 96, 138, 139]. The key factors they perceived would influence use and uptake of technology were decreased workload (rather than increased) [140], reliability of measurements aided by technology [146], ability to measure functional outcomes objectively [141], clearer criteria when choosing appropriate patients to be enrolled in the program [140], self-efficacy in the use of technology [94, 138, 146], and ease of reporting and tracking of patient data [90]. Patients and health care providers felt e-learning modules, push notifications, and appropriate feedback from sensors and virtual reality improved self-management [138, 142-144] (Figure 4).

Figure 4. Perceptions of patients and health care providers about the technology used. ADL: activities of daily living; FAQs: frequently asked questions; KR: knee replacement; mHealth: mobile health; VR: virtual reality.

Patient satisfaction levels were reported when teleconsultation was provided via a computer, smartphone, or tablet [34, 39, 55, 56, 80, 92, 105, 121, 123, 134, 135, 145, 147-149]; telephone [61], videoconferencing [38, 77, 94, 105, 139, 141, 150-152], a web-based system [32, 50, 90, 96, 140, 153], and an mHealth-enabled integrated care model [46, 88, 138]. Patients were satisfied with telemonitoring due to improved access to services, continued support after discharge from hospital, ability for self-management, reduced need for clinic visits, reduction in cost and travel time, ability of health care providers to provide personalized care [32, 61, 94, 121, 136, 138, 140, 141, 145, 153-155], ease of use [34, 50, 55, 56, 92, 105, 135, 138, 147, 148], motivation to perform exercises [134, 135], sense of security with remote monitoring [134, 155], and empathetic communication by a health care provider [121, 135, 136, 145, 152, 155]. The reasons for dissatisfaction were lack of an in-person examination, shorter appointment times, delay in receiving reports (eg, x-ray), and an inability to transfer pictures from one technology to another [140, 145, 149, 153]. Patients provided suggestions for the app functionalities to improve the ease of use such as minimal clicks, an instructional video for app navigation, and restriction of commercial advertisements [149]. Home modifications [149], emotional well-being, information related to activities of daily living in simple text, dietary advice, frequently asked questions, and use of traditional medicine for postoperative pain management were a few of the suggestions for app content [121].

Patients were generally satisfied with the telerehabilitation program and were ready to recommend it to others [39, 80, 85, 96, 121, 135, 151]. The use of technology for rehabilitation was influenced by computer literacy [141, 150]. However, interruption of virtual physiotherapy sessions due to poor internet issues [139] was not commonly reported (Figure 4).

Principal Findings

This scoping review summarized the extent, user perceptions, range, and nature of technologies used to support rehabilitation following knee arthroplasty. All studies reported in this review were from upper and middle-to-upper–income countries, with a steep increase in studies in the last decade. The technologies focused on enabling patients to remember prescribed exercises as well as be able to perform them appropriately by providing synchronous and asynchronous feedback via biosensors or virtual reality. Motivation and support during recovery via technology-enabled 1-way or 2-way communication gave patients access to health care providers. Self-management and monitoring of progress were dependent on active input using e-diaries by patients or passive input through wearables. In the context where these technologies were evaluated, end users were satisfied and found remote monitoring to be acceptable for routine use.

The last decade has seen an exponential increase in the number of arthroplasties worldwide [156]; however, a corresponding increase in technological solutions to facilitate remote monitoring is nonexistent in resource-limited settings such as LMICs where the need for monitoring and a continuum of care may be higher due to lower literacy levels and lack of access to rehabilitation clinics. Research on this topic that can inform clinical practice is nonexistent in the LMIC context. Despite a high penetration of the smartphone market [157] in LMICs, a higher initial investment to develop the technology, especially in the health care sector [158], or a lack of publication of such efforts could be reasons. In LMICs, there is an increasing trend of lower limb joint replacement procedures [156]. High out-of-pocket expenditures incurred due to home visits by physiotherapists or clinic visits by patients [159] dictate the need for a cost-effective and feasible technology-based strategy to fit the context while using lessons learned from available research.

There is unequivocal evidence that there is a need for physical and psychological support from professionals during the recovery period for pain management, adherence to exercises, and modifications to therapy planning based on one’s progress [3, 160, 161]. The apps were either focused on a single function (such as communication or knowledge transfer) or were multifunctional. They were generally received well by end users; however, the usability and acceptability of these applications or remote monitoring modalities cannot be extrapolated to low health literacy and tech literacy settings. The challenges we expect with using remote monitoring in the LMIC context could be inequitable smartphone access or tech literacy, internet speed, affordability of wearables, the burden to the health system if these needs are provided free of cost, and the need for educational content in multiple languages in countries with a non-native English-speaking, multilingual population such as in India [162].

Implications for Future Research

mHealth interventions have the potential to expand the reach and effectiveness of health support by facilitating behavior change. However, to ensure these “digital behavior health interventions” effectively engage users and are effective, both microengagement (the mHealth interface) and macroengagement (evidence-based behavior change techniques) are essential [163, 164]. However, we found only a handful of studies that reported user involvement during the development stage [28, 32, 51, 58, 64, 65, 75-77, 80, 86, 88, 115, 118, 169]. Studies rarely provided an adequate explanation of the theoretical behavioral framework behind the technology-based interventions [165].

Since the context and technologies are so varied, any new applications that are developed, especially in the LMIC context, should undertake formative research with end users to understand their needs, understand their preferences, and study the local digital regulatory requirements before investing time and effort. Feasibility and pilot testing by a multidisciplinary team should be crucial steps before a full-scale evaluation [69, 166], and embedding end users’ involvement and documenting their experiences at every stage are vital to refining future interventions [164]. Further, the rehabilitation protocols should map the application features with the desired function [167, 168], and this should be confirmed by means of a process evaluation embedded within the clinical evaluation to inform the mechanism of the impact in a real-life setting [147].


This review needs to be interpreted in light of the following limitations. This scoping review focused only on technology interventions for post-knee replacement rehabilitation and hence cannot be extrapolated to other orthopedic procedures. We did not include articles for which the full text was not available. Further, incomplete reporting on the features and functions of the technology is possible and may have affected our qualitative summary and conclusion.

We did not perform a consultation phase as per the guidelines [20], and the research question was formulated upon discussion between the researchers of the scoping review team, physiotherapists, and clinicians. We limited our search from 2001 onward; however, since knee arthroplasty and mHealth came into practice in the last 2 decades, this restriction in the search may not have an implication for our review findings.


Several technologies have been identified to promote adherence, increase self-efficacy, enhance self-management, and support remote monitoring. However, all the available technologies have been developed and used in developed countries. The need for remote monitoring is compelling in resource-limited countries where knee arthroplasty is on the rise. However, irrespective of the context, it is important to involve a multidisciplinary team and include users’ perspectives during the development stage.

What Was Already Known About the Topic

Computer and mobile technologies to support rehabilitation following knee arthroplasty are in wide use. Telerehabilitation and remote monitoring are as effective and safe as clinic-based rehabilitation programs. They reduce out-of-pocket expenditure or health cost expenditure by reducing the time to discharge following surgery and the number of clinic visits after discharge.

What This Study Adds

This study provides a map of the types of technology and the functionality of mobile and computer-based multifunction applications. We summarized end users’ perceptions and reasons for satisfaction or dissatisfaction with available technology. The findings reflect the lack of research and readily available technologies for LMICs.


The authors would like to thank distinguished Professor Gordon Guyatt, in the Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada, for providing expert comments. We also thank Dr. Shyamashree Biswas, Research Intern at the George institute, for assisting with the quality check of the extracted data.

This scoping review is a part of a fellowship funded by DBT/Wellcome Trust India Alliance (grant number: IA/CPHI/20/1/505224).

Authors' Contributions

ND conceptualized the protocol and conducted the search. ND, SP, PS, and ShP screened and extracted the data. The first draft was written by SP. RM and AK interpreted the study findings and gave significant feedback to the early drafts. RMad provided expert comments and suggestions and edited the later version of manuscript. ND takes responsibility for the data. All authors read and agreed to the final version of the manuscript.

Conflicts of Interest

None declared.

Multimedia Appendix 1

Supplementary information on search strategy, included and excluded studies.

DOCX File , 546 KB

Multimedia Appendix 2

Raw data extraction file for rehabilitation program studies.

XLSX File (Microsoft Excel File), 39 KB

Multimedia Appendix 3

PRISMA-ScR checklist.

PDF File (Adobe PDF File), 105 KB

  1. Zhang W, Moskowitz RW, Nuki G, Abramson S, Altman RD, Arden N, et al. OARSI recommendations for the management of hip and knee osteoarthritis, Part II: OARSI evidence-based, expert consensus guidelines. Osteoarthritis Cartilage. Feb 2008;16(2):137-162. [FREE Full text] [CrossRef] [Medline]
  2. Artz N, Elvers KT, Lowe CM, Sackley C, Jepson P, Beswick AD. Effectiveness of physiotherapy exercise following total knee replacement: systematic review and meta-analysis. BMC Musculoskelet Disord. Feb 07, 2015;16(1):15. [FREE Full text] [CrossRef] [Medline]
  3. Goldsmith LJ, Suryaprakash N, Randall E, Shum J, MacDonald V, Sawatzky R, et al. The importance of informational, clinical and personal support in patient experience with total knee replacement: a qualitative investigation. BMC Musculoskelet Disord. Mar 24, 2017;18(1):127. [FREE Full text] [CrossRef] [Medline]
  4. Groeneveld BS, Dekkers T, Mathijssen NMC, Vehmeijer SBW, Melles M, Goossens RHM. Communication preferences in total joint arthroplasty: exploring the patient experience through generative research. Orthop Nurs. 2020;39(5):292-302. [CrossRef] [Medline]
  5. Westby MD, Backman CL. Patient and health professional views on rehabilitation practices and outcomes following total hip and knee arthroplasty for osteoarthritis:a focus group study. BMC Health Serv Res. May 11, 2010;10(1):119. [FREE Full text] [CrossRef] [Medline]
  6. Mehta SJ, Hume E, Troxel AB, Reitz C, Norton L, Lacko H, et al. Effect of remote monitoring on discharge to home, return to activity, and rehospitalization after hip and knee arthroplasty: a randomized clinical trial. JAMA Netw Open. Dec 01, 2020;3(12):e2028328. [FREE Full text] [CrossRef] [Medline]
  7. Schwartz FH, Lange J. Factors that affect outcome following total joint arthroplasty: a review of the recent literature. Curr Rev Musculoskelet Med. Sep 29, 2017;10(3):346-355. [FREE Full text] [CrossRef] [Medline]
  8. L Snell D, Hipango J, Sinnott KA, Dunn JA, Rothwell A, Hsieh CJ, et al. Rehabilitation after total joint replacement: a scoping study. Disabil Rehabil. Jul 23, 2018;40(14):1718-1731. [CrossRef] [Medline]
  9. Alrawashdeh W, Eschweiler J, Migliorini F, El Mansy Y, Tingart M, Rath B. Effectiveness of total knee arthroplasty rehabilitation programmes: A systematic review and meta-analysis. J Rehabil Med. Jun 02, 2021;53(6):jrm00200. [FREE Full text] [CrossRef] [Medline]
  10. Buhagiar MA, Naylor JM, Harris IA, Xuan W, Adie S, Lewin A. Assessment of outcomes of inpatient or clinic-based vs home-based rehabilitation after total knee arthroplasty: a systematic review and meta-analysis. JAMA Netw Open. Apr 05, 2019;2(4):e192810. [CrossRef] [Medline]
  11. Jansson MM, Rantala A, Miettunen J, Puhto A, Pikkarainen M. The effects and safety of telerehabilitation in patients with lower-limb joint replacement: A systematic review and narrative synthesis. J Telemed Telecare. Apr 21, 2020;28(2):96-114. [CrossRef]
  12. Bernhardsson S, Larsson A, Bergenheim A, Ho-Henriksson C, Ekhammar A, Lange E, et al. Digital physiotherapy assessment vs conventional face-to-face physiotherapy assessment of patients with musculoskeletal disorders: A systematic review. PLoS One. Mar 21, 2023;18(3):e0283013. [FREE Full text] [CrossRef] [Medline]
  13. Peng L, Zeng Y, Wu Y, Si H, Shen B. Virtual reality-based rehabilitation in patients following total knee arthroplasty: a systematic review and meta-analysis of randomized controlled trials. Chin Med J (Engl). Dec 13, 2021;135(2):153-163. [FREE Full text] [CrossRef] [Medline]
  14. Constantinescu D, Pavlis W, Rizzo M, Vanden Berge D, Barnhill S, Hernandez VH. The role of commercially available smartphone apps and wearable devices in monitoring patients after total knee arthroplasty: a systematic review. EFORT Open Rev. Jul 05, 2022;7(7):481-490. [FREE Full text] [CrossRef] [Medline]
  15. Özden F, Sarı Z. The effect of mobile application-based rehabilitation in patients with total knee arthroplasty: A systematic review and meta-analysis. Arch Gerontol Geriatr. Oct 2023;113:105058. [CrossRef] [Medline]
  16. Gianzina E, Kalinterakis G, Delis S, Vlastos I, Platon Sachinis N, Yiannakopoulos CK. Evaluation of gait recovery after total knee arthroplasty using wearable inertial sensors: A systematic review. Knee. Mar 2023;41:190-203. [CrossRef] [Medline]
  17. McKeon JF, Alvarez PM, Vajapey AS, Sarac N, Spitzer AI, Vajapey SP. Expanding role of technology in rehabilitation after lower-extremity joint replacement: a systematic review. JBJS Rev. Sep 13, 2021;9(9):A. [CrossRef] [Medline]
  18. Wang X, Hunter DJ, Vesentini G, Pozzobon D, Ferreira ML. Technology-assisted rehabilitation following total knee or hip replacement for people with osteoarthritis: a systematic review and meta-analysis. BMC Musculoskelet Disord. Nov 03, 2019;20(1):506. [FREE Full text] [CrossRef] [Medline]
  19. Seron P, Oliveros MJ, Gutierrez-Arias R, Fuentes-Aspe R, Torres-Castro RC, Merino-Osorio C, et al. Effectiveness of telerehabilitation in physical therapy: a rapid overview. Phys Ther. Jun 01, 2021;101(6):A. [FREE Full text] [CrossRef] [Medline]
  20. Peters MDJ, Marnie C, Tricco AC, Pollock D, Munn Z, Alexander L, et al. Updated methodological guidance for the conduct of scoping reviews. JBI Evid Synth. Oct 2020;18(10):2119-2126. [CrossRef] [Medline]
  21. Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, Tunçalp; et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. Oct 02, 2018;169(7):467-473. [CrossRef]
  22. Pritwani S, Devasenapathy N. Mobile technology-based applications for rehabilitation monitoring and self-management after knee arthroplasty: A scoping review. Open Science Framework. Jun 21, 2022. URL: [accessed 2023-12-11]
  23. Rayyan. URL: [accessed 2023-12-11]
  24. Ayoade M, Morton L, Baillie L. Investigating the feasibility of a wireless motion capture system to aid in the rehabilitation of total knee replacement patients. Presented at: 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops; May 23-26, 2011, 2011; Dublin, Ireland. [CrossRef]
  25. Bäcker HC, Wu CH, Schulz MRG, Weber-Spickschen TS, Perka C, Hardt S. App-based rehabilitation program after total knee arthroplasty: a randomized controlled trial. Arch Orthop Trauma Surg. Sep 06, 2021;141(9):1575-1582. [FREE Full text] [CrossRef] [Medline]
  26. De Berardinis L, Senarighi M, Ciccullo C, Forte F, Spezia M, Gigante AP. Fast-track surgery and telerehabilitation protocol in unicompartmental knee arthroplasty leads to superior outcomes when compared with the standard protocol: a propensity-matched pilot study. Knee Surg Relat Res. Dec 12, 2022;34(1):44. [FREE Full text] [CrossRef] [Medline]
  27. Bitsaki M, Koutras G, Heep H, Koutras C. Cost-effective mobile-based healthcare system for managing total joint arthroplasty follow-up. Healthc Inform Res. Jan 2017;23(1):67-73. [FREE Full text] [CrossRef] [Medline]
  28. Blasco J, Roig-Casasús S, Igual-Camacho C, Díaz-Díaz B, Pérez-Maletzki J. Conversational chatbot to promote adherence to rehabilitation after total knee replacement: implementation and feasibility. Archives of Physical Medicine and Rehabilitation. Dec 2022;103(12):e125. [CrossRef]
  29. Bonora S, Pirani R, Amadori E, Fantini C, Chiari L, Merlo A, et al. Use of a haptic biofeedback in the rehabilitation of patients with total knee arthroplasty (TKA): A pilot study. Gait & Posture. Sep 2017;57:16-17. [CrossRef]
  30. Calliess T, Bocklage R, Karkosch R, Marschollek M, Windhagen H, Schulze M. Clinical evaluation of a mobile sensor-based gait analysis method for outcome measurement after knee arthroplasty. Sensors (Basel). Aug 28, 2014;14(9):15953-15964. [FREE Full text] [CrossRef] [Medline]
  31. Colomina J, Drudis R, Torra M, Pallisó F, Massip M, Vargiu E, et al. CONNECARE-Lleida Group. Implementing mHealth-enabled integrated care for complex chronic patients with osteoarthritis undergoing primary hip or knee arthroplasty: prospective, two-arm, parallel trial. J Med Internet Res. Sep 02, 2021;23(9):e28320. [FREE Full text] [CrossRef] [Medline]
  32. Correia FD, Nogueira A, Magalhães I, Guimarães J, Moreira M, Barradas I, et al. Medium-term outcomes of digital versus conventional home-based rehabilitation after total knee arthroplasty: prospective, parallel-group feasibility study. JMIR Rehabil Assist Technol. Feb 28, 2019;6(1):e13111. [FREE Full text] [CrossRef] [Medline]
  33. De Vroey H, Staes F, Weygers I, Vereecke E, Vanrenterghem J, Deklerck J, et al. The implementation of inertial sensors for the assessment of temporal parameters of gait in the knee arthroplasty population. Clin Biomech (Bristol, Avon). May 2018;54:22-27. [CrossRef] [Medline]
  34. Eichler S, Salzwedel A, Rabe S, Mueller S, Mayer F, Wochatz M, et al. The effectiveness of telerehabilitation as a supplement to rehabilitation in patients after total knee or hip replacement: randomized controlled trial. JMIR Rehabil Assist Technol. Nov 07, 2019;6(2):e14236. [FREE Full text] [CrossRef] [Medline]
  35. Gianola S, Stucovitz E, Castellini G, Mascali M, Vanni F, Tramacere I, et al. Effects of early virtual reality-based rehabilitation in patients with total knee arthroplasty: A randomized controlled trial. Medicine (Baltimore). Feb 2020;99(7):e19136. [FREE Full text] [CrossRef] [Medline]
  36. Gohir SA, Eek F, Kelly A, Abhishek A, Valdes AM. Effectiveness of internet-based exercises aimed at treating knee osteoarthritis: the iBEAT-OA randomized clinical trial. JAMA Netw Open. Feb 01, 2021;4(2):e210012. [FREE Full text] [CrossRef] [Medline]
  37. Gray J, McCarthy S, Carr E, Danjoux G, Hackett R, McCarthy A, et al. The impact of a digital joint school educational programme on post-operative outcomes following lower limb arthroplasty: a retrospective comparative cohort study. BMC Health Serv Res. Apr 29, 2022;22(1):580. [FREE Full text] [CrossRef] [Medline]
  38. Gündüz CS, Çalişkan N. The effect of preoperative video based pain training on postoperative pain and analgesic use in patients Undergoing Total Knee Arthroplasty: a non-randomized control group intervention study. Clin Nurs Res. Jul 31, 2021;30(6):741-752. [CrossRef] [Medline]
  39. Eisermann U, Haase I, Kladny B. Computer-aided multimedia training in orthopedic rehabilitation. Am J Phys Med Rehabil. Sep 2004;83(9):670-680. [CrossRef] [Medline]
  40. Hadamus A, Błażkiewicz M, Wydra KT, Kowalska AJ, Łukowicz M, Białoszewski D, et al. Effectiveness of early rehabilitation with exergaming in virtual reality on gait in patients after total knee replacement. J Clin Med. Aug 23, 2022;11(17):4950. [FREE Full text] [CrossRef] [Medline]
  41. Harmelink KEM, Zeegers AVCM, Tönis TM, Hullegie W, Nijhuis-van der Sanden MWG, Staal JB. The effectiveness of the use of a digital activity coaching system in addition to a two-week home-based exercise program in patients after total knee arthroplasty: study protocol for a randomized controlled trial. BMC Musculoskelet Disord. Jul 05, 2017;18(1):290. [FREE Full text] [CrossRef] [Medline]
  42. Janhunen M, Katajapuu N, Paloneva J, Pamilo K, Oksanen A, Keemu H, et al. Effects of a home-based, exergaming intervention on physical function and pain after total knee replacement in older adults: a randomised controlled trial. BMJ Open Sport Exerc Med. Mar 03, 2023;9(1):e001416. [FREE Full text] [CrossRef] [Medline]
  43. Jenny J. Measurement of the knee flexion angle with a smartphone-application is precise and accurate. J Arthroplasty. May 2013;28(5):784-787. [CrossRef] [Medline]
  44. Juhl C, Roth S, Schierbeck R, Nielsen L, Nordlien A, Hansen N, et al. Effectiveness of technology assisted exercise compared to usual care in total knee arthroplasty. Osteoarthritis and Cartilage. Apr 2016;24:S473. [CrossRef]
  45. Kontadakis G, Chasiouras D, Proimaki D, Halkiadakis M, Fyntikaki M, Mania K. Gamified platform for rehabilitation after total knee replacement surgery employing low cost and portable inertial measurement sensor node. Multimed Tools Appl. Sep 12, 2018;79(5-6):3161-3188. [CrossRef]
  46. Lebleu J, Pauwels A, Anract P, Parratte S, Van Overschelde P, Van Onsem S. Digital rehabilitation after knee arthroplasty: a multi-center prospective longitudinal cohort study. J Pers Med. May 13, 2023;13(5):824. [FREE Full text] [CrossRef] [Medline]
  47. Mark-Christensen T, Thorborg K, Kallemose T, Bandholm T. Physical rehabilitation versus no physical rehabilitation after total hip and knee arthroplasties: Protocol for a pragmatic, randomized, controlled, superiority trial (The DRAW1 trial). F1000Res. Feb 25, 2021;10:146. [CrossRef]
  48. Marques CJ, Bauer C, Grimaldo D, Tabeling S, Weber T, Ehlert A, et al. Sensor positioning influences the accuracy of knee ROM data of an e-rehabilitation system: a preliminary study with healthy subjects. Sensors (Basel). Apr 15, 2020;20(8):2237. [FREE Full text] [CrossRef] [Medline]
  49. Msayib Y, Gaydecki P, Callaghan M, Dale N, Ismail S. An intelligent remote monitoring system for total knee arthroplasty patients. J Med Syst. Jun 18, 2017;41(6):90. [FREE Full text] [CrossRef] [Medline]
  50. Nuevo M, Rodríguez-Rodríguez D, Jauregui R, Fabrellas N, Zabalegui A, Conti M, et al. Telerehabilitation following fast-track total knee arthroplasty is effective and safe: a randomized controlled trial with the ReHub platform. Disabil Rehabil. Jul 05, 2023:1-11. [CrossRef] [Medline]
  51. Osterloh J, Knaack F, Bader R, Behrens M, Peschers J, Nawrath L, et al. The effect of a digital-assisted group rehabilitation on clinical and functional outcomes after total hip and knee arthroplasty-a prospective randomized controlled pilot study. BMC Musculoskelet Disord. Mar 14, 2023;24(1):190. [FREE Full text] [CrossRef] [Medline]
  52. Pereira L, Rwakabayiza S, Lécureux E, Jolles B. Reliability of the knee smartphone-application goniometer in the acute orthopedic setting. J Knee Surg. Mar 24, 2017;30(3):223-230. [CrossRef] [Medline]
  53. Pfeufer D, Monteiro P, Gililland J, Anderson MB, Böcker W, Stagg M, et al. Immediate postoperative improvement in gait parameters following primary total knee arthroplasty can be measured with an insole sensor device. J Knee Surg. May 25, 2022;35(6):692-697. [CrossRef] [Medline]
  54. Piqueras M, Marco E, Coll M, Escalada F, Ballester A, Cinca C, et al. Effectiveness of an interactive virtual telerehabilitation system in patients after total knee arthoplasty: a randomized controlled trial. J Rehabil Med. Apr 2013;45(4):392-396. [FREE Full text] [CrossRef] [Medline]
  55. Pronk Y, Peters MCWM, Sheombar A, Brinkman J. Effectiveness of a mobile eHealth app in guiding patients in pain control and opiate use after total knee replacement: randomized controlled trial. JMIR Mhealth Uhealth. Mar 13, 2020;8(3):e16415. [FREE Full text] [CrossRef] [Medline]
  56. Scheper H, Derogee R, Mahdad R, van der Wal R, Nelissen R, Visser L, et al. A mobile app for postoperative wound care after arthroplasty: Ease of use and perceived usefulness. Int J Med Inform. Sep 2019;129:75-80. [CrossRef] [Medline]
  57. Boekesteijn RJ, Smolders JMH, Busch VJJF, Geurts ACH, Smulders K. Independent and sensitive gait parameters for objective evaluation in knee and hip osteoarthritis using wearable sensors. BMC Musculoskelet Disord. Mar 03, 2021;22(1):242. [FREE Full text] [CrossRef] [Medline]
  58. Stauber A, Schüßler N, Palmdorf S, Schürholz N, Bruns D, Osterbrink J, et al. RECOVER-E - a mobile app for patients undergoing total knee or hip replacement: study protocol. BMC Musculoskelet Disord. Feb 04, 2020;21(1):71. [FREE Full text] [CrossRef] [Medline]
  59. Straat AC, Maarleveld JM, Smit DJM, Visch L, Hulsegge G, Huirne JAF, et al. (Cost-)effectiveness of a personalized multidisciplinary eHealth intervention for knee arthroplasty patients to enhance return to activities of daily life, work and sports - rationale and protocol of the multicentre ACTIVE randomized controlled trial. BMC Musculoskelet Disord. Mar 04, 2023;24(1):162. [CrossRef] [Medline]
  60. Strahl A, Graichen H, Haas H, Hube R, Perka C, Rolvien T, et al. Evaluation of the patient-accompanying app "alley ortho companion" for patients with osteoarthritis of the knee and hip: study protocol for a randomized controlled multi-center trial. Trials. Aug 29, 2022;23(1):716. [FREE Full text] [CrossRef] [Medline]
  61. Szöts K, Konradsen H, Solgaard S, Bogø S, Østergaard B. Nurse-led telephone follow-up after total knee arthroplasty--content and the patients' views. J Clin Nurs. Oct 14, 2015;24(19-20):2890-2899. [CrossRef] [Medline]
  62. Timmers T, Janssen L, van der Weegen W, Das D, Marijnissen W, Hannink G, et al. The effect of an app for day-to-day postoperative care education on patients with total knee replacement: randomized controlled trial. JMIR Mhealth Uhealth. Oct 21, 2019;7(10):e15323. [FREE Full text] [CrossRef] [Medline]
  63. Torpil B, Kaya. The effectiveness of client-centered intervention with telerehabilitation method after total knee arthroplasty. OTJR (Thorofare N J). Jan 22, 2022;42(1):40-49. [CrossRef] [Medline]
  64. van Dijk-Huisman HC, Weemaes AT, Boymans TA, Lenssen AF, de Bie RA. Smartphone app with an accelerometer enhances patients' physical activity following elective orthopedic surgery: a pilot study. Sensors (Basel). Aug 02, 2020;20(15):4317. [FREE Full text] [CrossRef] [Medline]
  65. Pournajaf S, Goffredo M, Pellicciari L, Piscitelli D, Criscuolo S, Le Pera D, et al. Effect of balance training using virtual reality-based serious games in individuals with total knee replacement: A randomized controlled trial. Ann Phys Rehabil Med. Nov 2022;65(6):101609. [FREE Full text] [CrossRef] [Medline]
  66. Ficklscherer A, Stapf J, Meissner KM, Niethammer T, Lahner M, Wagenhäuser M, et al. Testing the feasibility and safety of the Nintendo Wii gaming console in orthopedic rehabilitation: a pilot randomized controlled study. Arch Med Sci. Dec 01, 2016;12(6):1273-1278. [FREE Full text] [CrossRef] [Medline]
  67. Alexander JS, Redfern RE, Duwelius PJ, Berend KR, Lombardi AV, Crawford DA. Use of a smartphone-based care platform after primary partial and total knee arthroplasty: 1-year follow-up of a prospective randomized controlled trial. J Arthroplasty. Jul 2023;38(7 Suppl 2):S208-S214. [CrossRef] [Medline]
  68. Antunes R, Jacob P, Meyer A, Conditt MA, Roche MW, Verstraete MA. Accuracy of measuring knee flexion after TKA through wearable IMU sensors. J Funct Morphol Kinesiol. Jul 05, 2021;6(3):60. [FREE Full text] [CrossRef] [Medline]
  69. Bade M, Cheuy V, Zeni J, Christiansen C, Stevens-Lapsley J. Effects Of real-time biofeedback using instrumented insoles on recovery after total knee arthroplasty: a pilot study. Osteoarthritis and Cartilage. Mar 2023;31:S124. [CrossRef]
  70. Prvu Bettger J, Green CL, Holmes DN, Chokshi A, Mather RC, Hoch BT, et al. Effects of virtual exercise rehabilitation in-home therapy compared with traditional care after total knee arthroplasty. The Journal of Bone and Joint Surgery. Nov 18, 2019;102(2):101-109. [CrossRef]
  71. Bini S, Mahajan J. Clinical outcomes of remote asynchronous telerehabilitation are equivalent to traditional therapy following total knee arthroplasty: A randomized control study. J Telemed Telecare. Jul 09, 2016;23(2):239-247. [CrossRef]
  72. Campbell KJ, Louie PK, Bohl DD, Edmiston T, Mikhail C, Li J, et al. A novel, automated text-messaging system is effective in patients undergoing total joint arthroplasty. The Journal of Bone and Joint Surgery. Jan 16, 2019;101(2):145-151. [CrossRef]
  73. Chapman RM, Moschetti WE, Van Citters DW. Is clinically measured knee range of motion after total knee arthroplasty ‘good enough?’: A feasibility study using wearable inertial measurement units to compare knee range of motion captured during physical therapy versus at home. Medicine in Novel Technology and Devices. Sep 2021;11:100085. [CrossRef]
  74. Christensen JC, Stanley EC, Oro EG, Carlson HB, Naveh YY, Shalita R, et al. The validity and reliability of the OneStep smartphone application under various gait conditions in healthy adults with feasibility in clinical practice. J Orthop Surg Res. Sep 14, 2022;17(1):417. [FREE Full text] [CrossRef] [Medline]
  75. Chughtai M, Shah NV, Sultan AA, Solow M, Tiberi JV, Mehran N, et al. The role of prehabilitation with a telerehabilitation system prior to total knee arthroplasty. Ann Transl Med. Feb 2019;7(4):68-68. [FREE Full text] [CrossRef] [Medline]
  76. Chughtai M, Kelly J, Newman J, Sultan A, Khlopas A, Sodhi N, et al. The role of virtual rehabilitation in total and unicompartmental knee arthroplasty. J Knee Surg. Jan 16, 2018;32(1):105-110. [CrossRef] [Medline]
  77. Doiron-Cadrin P, Kairy D, Vendittoli P, Lowry V, Poitras S, Desmeules F. Feasibility and preliminary effects of a tele-prehabilitation program and an in-person prehablitation program compared to usual care for total hip or knee arthroplasty candidates: a pilot randomized controlled trial. Disabil Rehabil. Apr 13, 2020;42(7):989-998. [CrossRef] [Medline]
  78. LeBrun DG, Martino B, Biehl E, Fisher CM, Gonzalez Della Valle A, Ast MP. Telerehabilitation has similar clinical and patient-reported outcomes compared to traditional rehabilitation following total knee arthroplasty. Knee Surg Sports Traumatol Arthrosc. Dec 26, 2022;30(12):4098-4103. [CrossRef] [Medline]
  79. Fung V, Ho A, Shaffer J, Chung E, Gomez M. Use of Nintendo Wii Fit in the rehabilitation of outpatients following total knee replacement: a preliminary randomised controlled trial. Physiotherapy. Sep 2012;98(3):183-188. [CrossRef] [Medline]
  80. Hong M, Loeb J, Yang M, Bailey JF. Postoperative outcomes of a digital rehabilitation program after total knee arthroplasty: retrospective, observational feasibility study. JMIR Form Res. Sep 19, 2022;6(9):e40703. [FREE Full text] [CrossRef] [Medline]
  81. Klement MR, Rondon AJ, McEntee RM, Greenky MR, Austin MS. Web-based, self-directed physical therapy after total knee arthroplasty is safe and effective for most, but not all, patients. J Arthroplasty. Jul 2019;34(7S):S178-S182. [CrossRef] [Medline]
  82. Kline PW, Melanson EL, Sullivan WJ, Blatchford PJ, Miller MJ, Stevens-Lapsley JE, et al. Improving physical activity through adjunct telerehabilitation following total knee arthroplasty: randomized controlled trial protocol. Phys Ther. Jan 01, 2019;99(1):37-45. [FREE Full text] [CrossRef] [Medline]
  83. Knapp PW, Keller RA, Mabee KA, Pillai R, Frisch NB. Quantifying patient engagement in total joint arthroplasty using digital application-based technology. J Arthroplasty. Sep 2021;36(9):3108-3117. [FREE Full text] [CrossRef] [Medline]
  84. Krebs DE, Huddleston JI, Goldvasser D, Scarborough DM, Harris WH, Malchau H. Biomotion community-wearable human activity monitor: total knee replacement and healthy control subjects. Presented at: International Workshop on Wearable and Implantable Body Sensor Networks (BSN'06); April 3-5, 2006; Cambridge, MA. [CrossRef]
  85. Kuether J, Moore A, Kahan J, Martucci J, Messina T, Perreault R, et al. Telerehabilitation for total hip and knee arthroplasty patients: a pilot series with high patient satisfaction. HSS J. Oct 21, 2019;15(3):221-225. [FREE Full text] [CrossRef] [Medline]
  86. Lam AWK, Varona-Marin D, Li Y, Fergenbaum M, Kulić D. Automated rehabilitation system: movement measurement and feedback for patients and physiotherapists in the rehabilitation clinic. Human–Computer Interaction. Nov 18, 2016;31(3):294-334. [CrossRef]
  87. Losina E, Collins JE, Daigle ME, Donnell-Fink LA, Prokopetz JJ, Strnad D, et al. The AViKA (Adding Value in Knee Arthroplasty) postoperative care navigation trial: rationale and design features. BMC Musculoskelet Disord. Oct 12, 2013;14(1):290. [FREE Full text] [CrossRef] [Medline]
  88. Milliren CE, Lindsay B, Biernat L, Smith TA, Weaver B. Can digital engagement improve outcomes for total joint replacements? Digit Health. Apr 24, 2022;8:20552076221095322. [FREE Full text] [CrossRef] [Medline]
  89. Na A, Buchanan TS. Validating wearable sensors using self-reported instability among patients with knee osteoarthritis. PM R. Feb 12, 2021;13(2):119-127. [CrossRef] [Medline]
  90. Bell KM, Onyeukwu C, Smith CN, Oh A, Devito Dabbs A, Piva SR, et al. A portable system for remote rehabilitation following a total knee replacement: a pilot randomized controlled clinical study. Sensors (Basel). Oct 27, 2020;20(21):6118. [FREE Full text] [CrossRef] [Medline]
  91. Pellegrini CA, Lee J, DeVivo KE, Harpine CE, Del Gaizo DJ, Wilcox S. Reducing sedentary time using an innovative mHealth intervention among patients with total knee replacement: Rationale and study protocol. Contemp Clin Trials Commun. Jun 2021;22:100810. [FREE Full text] [CrossRef] [Medline]
  92. Ramkumar PN, Haeberle HS, Ramanathan D, Cantrell WA, Navarro SM, Mont MA, et al. Remote patient monitoring using mobile health for total knee arthroplasty: validation of a wearable and machine learning-based surveillance platform. J Arthroplasty. Oct 2019;34(10):2253-2259. [CrossRef] [Medline]
  93. Summers SH, Nunley RM, Slotkin EM. A home-based, remote-clinician-controlled, physical therapy device leads to superior outcomes when compared to standard physical therapy for rehabilitation after total knee arthroplasty. J Arthroplasty. Mar 2023;38(3):497-501. [CrossRef] [Medline]
  94. Tousignant M, Boissy P, Moffet H, Corriveau H, Cabana F, Marquis F, et al. Patients' satisfaction of healthcare services and perception with in-home telerehabilitation and physiotherapists' satisfaction toward technology for post-knee arthroplasty: an embedded study in a randomized trial. Telemed J E Health. Jun 2011;17(5):376-382. [CrossRef] [Medline]
  95. Tripuraneni KR, Foran JR, Munson NR, Racca NE, Carothers JT. A smartwatch paired with a mobile application provides postoperative self-directed rehabilitation without compromising total knee arthroplasty outcomes: a randomized controlled trial. J Arthroplasty. Dec 2021;36(12):3888-3893. [CrossRef] [Medline]
  96. Visperas AT, Greene KA, Krebs VE, Klika AK, Piuzzi NS, Higuera-Rueda CA. A web-based interactive patient-provider software platform does not increase patient satisfaction or decrease hospital resource utilization in total knee and hip arthroplasty patients in a single large hospital system. J Arthroplasty. Jul 2021;36(7):2290-2296.e1. [CrossRef] [Medline]
  97. Youn I, Youn J, Zeni J, Knarr B. Biomechanical gait variable estimation using wearable sensors after unilateral total knee arthroplasty. Sensors (Basel). May 15, 2018;18(5):1577. [FREE Full text] [CrossRef] [Medline]
  98. Zhao W, Yang S, Luo X. Towards rehabilitation at home after total knee replacement. Tsinghua Science and Technology. 2021;26(6):791-799. [CrossRef]
  99. Kramer JF, Speechley M, Bourne R, Rorabeck C, Vaz M. Comparison of clinic- and home-based rehabilitation programs after total knee arthroplasty. Clin Orthop Relat Res. May 2003(410):225-234. [CrossRef] [Medline]
  100. Bolam SM, Batinica B, Yeung TC, Weaver S, Cantamessa A, Vanderboor TC, et al. Remote patient monitoring with wearable sensors following knee arthroplasty. Sensors (Basel). Jul 29, 2021;21(15):5143. [FREE Full text] [CrossRef] [Medline]
  101. Castle H, Kozak K, Sidhu A, Khan RJK, Haebich S, Bowden V, et al. Smartphone technology: a reliable and valid measure of knee movement in knee replacement. Int J Rehabil Res. Jun 2018;41(2):152-158. [CrossRef] [Medline]
  102. Levinger P, Zeina D, Teshome AK, Skinner E, Begg R, Abbott JH. A real time biofeedback using Kinect and Wii to improve gait for post-total knee replacement rehabilitation: a case study report. Disabil Rehabil Assist Technol. Sep 04, 2016;11(3):251-262. [CrossRef] [Medline]
  103. Liptak MG, Theodoulou A, Kaambwa B, Saunders S, Hinrichs SW, Woodman RJ, et al. The safety, efficacy and cost-effectiveness of the Maxm Skate, a lower limb rehabilitation device for use following total knee arthroplasty: study protocol for a randomised controlled trial. Trials. Jan 10, 2019;20(1):36. [FREE Full text] [CrossRef] [Medline]
  104. Negus J, Cawthorne D, Chen J, Scholes C, Parker D, March L. Patient outcomes using Wii-enhanced rehabilitation after total knee replacement - the TKR-POWER study. Contemp Clin Trials. Jan 2015;40:47-53. [CrossRef] [Medline]
  105. Russell TG, Buttrum P, Wootton R, Jull GA. Internet-based outpatient telerehabilitation for patients following total knee arthroplasty. The Journal of Bone and Joint Surgery-American Volume. 2011;93(2):113-120. [CrossRef]
  106. Duong V, Dennis S, Harris A, Robbins S, Venkatesha V, Ferreira M, et al. The effects of a disruptive digital technology intervention following total knee replacement: results from the pathway randomised controlled trial. Osteoarthritis and Cartilage. Mar 2023;31:S30-S31. [CrossRef]
  107. Hardwick-Morris M, Carlton S, Twiggs J, Miles B, Liu D. Pre- and postoperative physiotherapy using a digital application decreases length of stay without reducing patient outcomes following total knee arthroplasty. Arthroplasty. Aug 02, 2022;4(1):30. [FREE Full text] [CrossRef] [Medline]
  108. Farr-Wharton G, Li J, Hussain MS, Freyne J. Mobile Supported Health Services: Experiences in Orthopaedic Care. Presented at: IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS); July 28-30, 2020; Rochester, MN. [CrossRef]
  109. McDonall J, de Steiger R, Reynolds J, Redley B, Livingston PM, Hutchinson AF, et al. Patient activation intervention to facilitate participation in recovery after total knee replacement (MIME): a cluster randomised cross-over trial. BMJ Qual Saf. Oct 11, 2019;28(10):782-792. [FREE Full text] [CrossRef] [Medline]
  110. An J, Ryu H, Lyu S, Yi H, Lee B. Effects of preoperative telerehabilitation on muscle strength, range of motion, and functional outcomes in candidates for total knee arthroplasty: a single-blind randomized controlled trial. Int J Environ Res Public Health. Jun 04, 2021;18(11):6071. [FREE Full text] [CrossRef] [Medline]
  111. Chiang C, Chen K, Liu K, Hsu S, Chan C. Data collection and analysis using wearable sensors for monitoring knee range of motion after total knee arthroplasty. Sensors (Basel). Feb 22, 2017;17(2):418. [FREE Full text] [CrossRef] [Medline]
  112. Huang Y, Liu Y, Hsu W, Lai L, Lee MS. Progress on range of motion after total knee replacement by sensor-based system. Sensors (Basel). Mar 18, 2020;20(6):1703. [FREE Full text] [CrossRef] [Medline]
  113. Huang P, He J, Zhang YM. [The mobile application of patient management in education and follow-up for patients following total knee arthroplasty]. Zhonghua Yi Xue Za Zhi. May 30, 2017;97(20):1592-1595. [CrossRef] [Medline]
  114. Hung LP, Chao YH, Tseng YL, Chung YL. Constructing a home-based knee replacement exercise monitoring system with G sensor. Presented at: International Conference on Frontier Computing; July 3-6, 2018; Kuala Lumpur, Malaysia. [CrossRef]
  115. Li Y, Gu Z, Ning R, Yin H. Study on the effect of internet plus continuous nursing on functional recovery and medication compliance of patients with knee joint replacement. J Orthop Surg Res. Jun 11, 2023;18(1):424. [FREE Full text] [CrossRef] [Medline]
  116. Lou N, Diao Y, Chen Q, Ning Y, Li G, Liang S, et al. A portable wearable inertial system for rehabilitation monitoring and evaluation of patients with total knee replacement. Front Neurorobot. Mar 23, 2022;16:836184. [FREE Full text] [CrossRef] [Medline]
  117. Lu G, Wu T, Tan Q, Wu Z, Shi L, Zhong Y. The effect of a micro-visual intervention on the accelerated recovery of patients with kinesiophobia after total knee replacement during neo-coronary pneumonia. Medicine (Baltimore). Feb 12, 2021;100(6):e24141. [FREE Full text] [CrossRef] [Medline]
  118. Park KH, Song MR. The effects of postdischarge telephone counseling and short message service on the knee function, activities of daily living, and life satisfaction of patients undergoing total knee replacement. Orthop Nurs. 2017;36(3):229-236. [FREE Full text] [CrossRef] [Medline]
  119. Park SA, Jeong Y. The effect of a multidimensional home rehabilitation program for post-total knee arthroplasty elderly patients. Orthop Nurs. 2023;42(1):22-32. [CrossRef] [Medline]
  120. Su C, Cheng C. Developing and evaluating creativity gamification rehabilitation system: the application of PCA-ANFIS based emotions model. Eurasia J Math Sci T. 2016;12(5):1. [CrossRef]
  121. Wang Q, Hunter S, Lee RL, Chan SW. The effectiveness of a mobile application-based programme for rehabilitation after total hip or knee arthroplasty: A randomised controlled trial. Int J Nurs Stud. Apr 2023;140:104455. [FREE Full text] [CrossRef] [Medline]
  122. Yang C, Shang L, Yao S, Ma J, Xu C. Cost, time savings and effectiveness of wearable devices for remote monitoring of patient rehabilitation after total knee arthroplasty: study protocol for a randomized controlled trial. J Orthop Surg Res. Jun 27, 2023;18(1):461. [FREE Full text] [CrossRef] [Medline]
  123. Zhang X, Chen X, Kourkoumelis N, Gao R, Li G, Zhu C. A social media-promoted educational community of joint replacement patients using the WeChat app: survey study. JMIR Mhealth Uhealth. Mar 18, 2021;9(3):e18763. [FREE Full text] [CrossRef] [Medline]
  124. Zheng Q, Chen H. A monitoring system for walking rehabilitation after THR or TKR surgeries. Annu Int Conf IEEE Eng Med Biol Soc. Jul;2017:2373-2376. [CrossRef] [Medline]
  125. Zhang H, Zhou Y. Concept verification of a remote automatic scoring system for evaluating knee function after total knee arthroplasty. J Knee Surg. Mar 27, 2021;34(4):464-470. [CrossRef] [Medline]
  126. Jansson M, Vuorinen A, Harjumaa M, Similä H, Koivisto J, Puhto A, et al. The digital patient journey solution for patients undergoing elective hip and knee arthroplasty: Protocol for a pragmatic randomized controlled trial. J Adv Nurs. Jun 15, 2020;76(6):1436-1448. [CrossRef] [Medline]
  127. Hadamus A, Białoszewski D, Urbaniak E, Kowalska A, Wydra K, Boratyński R, et al. The impact of training in virtual reality on balance in patients after total knee replacement is relatively slight. Gait & Posture. Sep 2020;81:134-135. [CrossRef]
  128. Wang Q, Hunter S, Lee RL, Wang X, Chan SW. Patients' needs regarding rehabilitation services delivered via mobile applications after arthroplasty: A qualitative study. J Clin Nurs. Nov 02, 2022;31(21-22):3178-3189. [CrossRef] [Medline]
  129. Doak CC, Doak LG, Root JH. Teaching Patients with Low Literacy Skills. Philadelphia, PA. J. B. Lippincott Company; 1996.
  130. Net Promoter System. URL: [accessed 2023-12-12]
  131. Demiris G, Speedie S, Finkelstein S. A questionnaire for the assessment of patients' impressions of the risks and benefits of home telecare. J Telemed Telecare. Jun 24, 2000;6(5):278-284. [CrossRef] [Medline]
  132. Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly. Sep 1989;13(3):319-340. [FREE Full text] [CrossRef]
  133. Parmanto B, Lewis AN, Graham KM, Bertolet MH. Development of the Telehealth Usability Questionnaire (TUQ). Int J Telerehabil. Jul 01, 2016;8(1):3-10. [FREE Full text] [CrossRef] [Medline]
  134. Cooper DM, Bhuskute N, Walsh G. Exploring the impact and acceptance of wearable sensor technology for pre- and postoperative rehabilitation in knee replacement patients: a U.K.-based pilot study. JB JS Open Access. 2022;7(2):1. [FREE Full text] [CrossRef] [Medline]
  135. Booth MW, Riegler V, King JS, Barrack RL, Hannon CP. Patients' perceptions of remote monitoring and app-based rehabilitation programs: a comparison of total hip and knee arthroplasty. J Arthroplasty. Jul 2023;38(7S):S39-S43. [CrossRef] [Medline]
  136. van Kasteren Y, Freyne J, Hussain MS. Total knee replacement and the effect of technology on cocreation for improved outcomes and delivery: qualitative multi-stakeholder study. J Med Internet Res. Mar 20, 2018;20(3):e95. [FREE Full text] [CrossRef] [Medline]
  137. Jansson MM, Harjumaa M, Puhto A, Pikkarainen M. Healthcare professionals' proposed eHealth needs in elective primary fast-track hip and knee arthroplasty journey: A qualitative interview study. J Clin Nurs. Dec 18, 2019;28(23-24):4434-4446. [CrossRef] [Medline]
  138. de Batlle J, Massip M, Vargiu E, Nadal N, Fuentes A, Ortega Bravo M, et al. CONNECARE-Lleida Group. Implementing mobile health-enabled integrated care for complex chronic patients: patients and professionals' acceptability study. JMIR Mhealth Uhealth. Nov 20, 2020;8(11):e22136. [FREE Full text] [CrossRef] [Medline]
  139. Boissy P, Tousignant M, Moffet H, Nadeau S, Brière S, Mérette C, Belzile; et al. Conditions of use, reliability, and quality of audio/video-mediated communications during in-home rehabilitation teletreatment for postknee arthroplasty. Telemed J E Health. Aug 2016;22(8):637-649. [CrossRef] [Medline]
  140. Parkes RJ, Palmer J, Wingham J, Williams DH. Is virtual clinic follow-up of hip and knee joint replacement acceptable to patients and clinicians? A sequential mixed methods evaluation. BMJ Open Qual. Mar 01, 2019;8(1):e000502. [FREE Full text] [CrossRef] [Medline]
  141. Russell TG, Buttrum P, Wootton R, Jull GA. Low-bandwidth telerehabilitation for patients who have undergone total knee replacement: preliminary results. J Telemed Telecare. Dec 02, 2003;9 Suppl 2:S44-S47. [CrossRef] [Medline]
  142. Lee M, Suh D, Son J, Kim J, Eun S, Yoon B. Patient perspectives on virtual reality-based rehabilitation after knee surgery: Importance of level of difficulty. J Rehabil Res Dev. 2016;53(2):239-252. [CrossRef]
  143. Reid H, Mohammadi S, Watson W, Robillard JM, Crocker M, Westby MD, et al. Patient and caregiver perspectives on an eHealth tool: a qualitative investigation of preferred formats, features and characteristics of a presurgical eHealth education module. Rehabil Process Outcome. Apr 21, 2021;10:11795727211010501. [FREE Full text] [CrossRef] [Medline]
  144. Culliton SE, Bryant DM, MacDonald SJ, Hibbert KM, Chesworth BM. Effect of an e-Learning tool on expectations and satisfaction following total knee arthroplasty: a randomized controlled trial. J Arthroplasty. Jul 2018;33(7):2153-2158. [CrossRef] [Medline]
  145. LeBrun DG, Malfer C, Wilson M, Carroll KM, Wang Ms V, Mayman DJ, et al. Telemedicine in an outpatient arthroplasty setting during the COVID-19 pandemic: early lessons from New York City. HSS J. Feb 21, 2021;17(1):25-30. [FREE Full text] [CrossRef] [Medline]
  146. Argent R, Slevin P, Bevilacqua A, Neligan M, Daly A, Caulfield B. Clinician perceptions of a prototype wearable exercise biofeedback system for orthopaedic rehabilitation: a qualitative exploration. BMJ Open. Oct 25, 2018;8(10):e026326. [CrossRef] [Medline]
  147. McDonall J, Redley B, Livingston P, Hutchinson A, de Steiger R, Botti M. A nurse-led multimedia intervention to increase patient participation in recovery after knee arthroplasty: hybrid type II implementation study. JMIR Hum Factors. May 19, 2022;9(2):e36959. [FREE Full text] [CrossRef] [Medline]
  148. Giunta NM, Paladugu PS, Bernstein DN, Makhni MC, Chen AF. Telemedicine hip and knee arthroplasty experience during COVID-19. J Arthroplasty. Aug 2022;37(8S):S814-S818.e2. [FREE Full text] [CrossRef] [Medline]
  149. Joshi R, Joseph A, Mihandoust S, Madathil KC, Cotten SR. A mobile application-based home assessment tool for patients undergoing joint replacement surgery: a qualitative feasibility study. Appl Ergon. Sep 2022;103:103796. [CrossRef] [Medline]
  150. Glinkowski W, Cabaj D, Kostrubała A, Krawczak K, Górecki A. Pre-surgery and post-surgery telerehabilitation for hip and knee replacement - Treatment options review and patient's attitudes towards telerehabilitation. Presented at: eChallenges e-2010 Conference; October 27-29, 2010, 2011; Warsaw, Poland.
  151. Moffet H, Tousignant M, Nadeau S, Mérette C, Boissy P, Corriveau H, Belzile; et al. Patient satisfaction with in-home telerehabilitation after total knee arthroplasty: results from a randomized controlled trial. Telemed J E Health. Feb 2017;23(2):80-87. [CrossRef] [Medline]
  152. Williams E, Putnam J, Emkes L, Greenwood J. Patient perspectives and numerical evaluation of a COVID secure hybrid rehabilitation programme following knee replacement surgery. Physiotherapy. Feb 2022;114:e224-e225. [CrossRef]
  153. Marsh J, Bryant D, MacDonald SJ, Naudie D, Remtulla A, McCalden R, et al. Are patients satisfied with a web-based followup after total joint arthroplasty? Clin Orthop Relat Res. Jun 2014;472(6):1972-1981. [FREE Full text] [CrossRef] [Medline]
  154. Grant S, Blom AW, Whitehouse MR, Craddock I, Judge A, Tonkin EL, et al. Using home sensing technology to assess outcome and recovery after hip and knee replacement in the UK: the HEmiSPHERE study protocol. BMJ Open. Jul 28, 2018;8(7):e021862. [FREE Full text] [CrossRef] [Medline]
  155. Kairy D, Tousignant M, Leclerc N, Côté AM, Levasseur M, Researchers T. The patient's perspective of in-home telerehabilitation physiotherapy services following total knee arthroplasty. Int J Environ Res Public Health. Aug 30, 2013;10(9):3998-4011. [FREE Full text] [CrossRef] [Medline]
  156. Hip and knee replacement. OECD iLibrary. URL: [accessed 2023-12-12]
  157. Silver L. Smartphone Ownership Is Growing Rapidly Around the World, But Not Always Equally. Pew Research Center. Feb 05, 2019. URL: [accessed 2023-12-12]
  158. McCool J, Dobson R, Whittaker R, Paton C. Mobile health (mHealth) in low- and middle-income countries. Annu Rev Public Health. Apr 05, 2022;43(1):525-539. [FREE Full text] [CrossRef] [Medline]
  159. Malik IV, Devasenapathy N, Kumar A, Dogra H, Ray S, Gautam D, et al. Estimation of expenditure and challenges related to rehabilitation after knee arthroplasty: a hospital-based cross-sectional study. Indian J Orthop. Oct 02, 2021;55(5):1317-1325. [FREE Full text] [CrossRef] [Medline]
  160. Zhou Z, Hou Y, Lin J, Wang K, Liu Q. Patients' views toward knee osteoarthritis exercise therapy and factors influencing adherence - a survey in China. Phys Sportsmed. May 16, 2018;46(2):221-227. [CrossRef] [Medline]
  161. Taylor CE, Murray CM, Stanton TR. Patient perspectives of pain and function after knee replacement: a systematic review and meta-synthesis of qualitative studies. PR9. May 09, 2022;7(3):e1006. [CrossRef]
  162. Reddy H, Joshi S, Joshi A, Wagh V. A critical review of global digital divide and the role of technology in healthcare. Cureus. Sep 2022;14(9):e29739. [CrossRef] [Medline]
  163. Perski O, Blandford A, West R, Michie S. Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis. Transl Behav Med. Jun 13, 2017;7(2):254-267. [FREE Full text] [CrossRef] [Medline]
  164. Voorheis P, Zhao A, Kuluski K, Pham Q, Scott T, Sztur P, et al. Integrating behavioral science and design thinking to develop mobile health interventions: systematic scoping review. JMIR Mhealth Uhealth. Mar 16, 2022;10(3):e35799. [FREE Full text] [CrossRef] [Medline]
  165. Hussain MS, Li J, Brindal E, van Kasteren Y, Varnfield M, Reeson A, et al. Supporting the delivery of total knee replacements care for both patients and their clinicians with a mobile app and web-based tool: randomized controlled trial protocol. JMIR Res Protoc. Mar 01, 2017;6(3):e32. [FREE Full text] [CrossRef] [Medline]
  166. Bade MJ, Christensen JC, Zeni JA, Christiansen CL, Dayton MR, Forster JE, et al. Movement pattern biofeedback training after total knee arthroplasty: Randomized clinical trial protocol. Contemp Clin Trials. Apr 2020;91:105973. [FREE Full text] [CrossRef] [Medline]
  167. Wang X, Hunter DJ, Robbins S, Capistrano S, Duong V, Melo L, et al. Participatory health through behavioural engagement and disruptive digital technology for postoperative rehabilitation: protocol of the PATHway trial. BMJ Open. Jan 17, 2021;11(1):e041328. [FREE Full text] [CrossRef] [Medline]
  168. McDonall J, de Steiger R, Reynolds J, Redley B, Livingston P, Botti M. Patient participation in postoperative care activities in patients undergoing total knee replacement surgery: Multimedia Intervention for Managing patient Experience (MIME). Study protocol for a cluster randomised crossover trial. BMC Musculoskelet Disord. Jul 18, 2016;17(1):294. [FREE Full text] [CrossRef] [Medline]
  169. Argent R, Slevin P, Bevilacqua A, Neligan M, Daly A, Caulfield B. Wearable sensor-based exercise biofeedback for orthopaedic rehabilitation: a mixed methods user evaluation of a prototype system. Sensors (Basel). Jan 21, 2019;19(2):432. [FREE Full text] [CrossRef] [Medline]
  170. Szöts K, Konradsen H, Solgaard S, Østergaard B. Telephone follow-up by nurse after total knee arthroplasty: results of a randomized clinical trial. Orthop Nurs. 2016;35(6):411-420. [CrossRef] [Medline]

CENTRAL: Cochrane Central Register of Controlled Trials
JBI: Joanna Briggs Institute
LMIC: low and middle-income countries
mHealth: mobile health
PRISMA-ScR: Preferred Reporting Items for Systematic Review and Meta-Analyses Extension for Scoping Reviews
RCT: randomized controlled trial
ROM: range of motion

Edited by L Buis; submitted 04.04.23; peer-reviewed by A Timmermans, T Li; comments to author 03.07.23; revised version received 10.10.23; accepted 01.12.23; published 26.01.24.


©Sabhya Pritwani, Purnima Shrivastava, Shruti Pandey, Ajit Kumar, Rajesh Malhotra, Ralph Maddison, Niveditha Devasenapathy. Originally published in JMIR mHealth and uHealth (, 26.01.2024.

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