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Abstract

Background:

Diabetes mellitus leads to a high risk of knee osteoarthritis, which would require total knee arthroplasty (TKA). Functional rehabilitation is often used for TKA, and telerehabilitation is gaining popularity in postoperative functional rehabilitation. The purpose of this study is to examine whether the inertial measurement unit (IMU) sensor can improve the functional results of telerehabilitation.

Method:

This prospective randomized controlled trial included participants from a single orthopedic center in China. In total, 131 patients participated in this study, which was a two-arm, single-assessor-blinded, randomized controlled trial. Participants were randomly assigned to one of two groups the telerehabilitation group with IMU sensor-based feedback assistance (IMU group) or without IMU sensor-based feedback assistance (TELE group). The TELE group received a 6-week home-based rehabilitation program with video instructions on a tablet and remote coaching, whereas the IMU group received the same rehabilitation program and remote coaching; the program was assisted by an inertial sensor system. Patients were evaluated at baseline, hospital discharge, 8 weeks, and 24 weeks after surgery. The primary outcome measure was the Knee Injury and Osteoarthritis Outcome Score (KOOS). Secondary outcome measures included range of motion, functional, and strength tests.

Results:

A total of 116 participants completed this study (59 in IMU, 57 in TELE group). No significant demographic differences were noted. KOOSs and muscle strength in both groups improved over time. There were significant benefits of IMU group in most comparisons at the 24-week assessments, adjusted for baseline value.

Conclusion:

Our results showed that IMU sensor can improve functional results of telerehabilitation in diabetic patients after TKA.

Trial Registration:

ChiCTR2100053313.

Introduction

Diabetes mellitus is closely associated with the development of knee osteoarthritis (KOA), the most common joint disease affecting the knee joint and a major source of disability.¹ Elevated levels of glucose in the blood can result in the glycation of proteins found in cartilage, particularly those with slow turnover, like type II collagen. This glycation can alter the physical characteristics of these proteins, leading to increased stiffness in the collagen network of the cartilage and decreased ability to withstand mechanical stress. In addition, recent research has emphasized the significant involvement of advanced glycation end products and their receptors in the inflammatory and degradative processes associated with osteoarthritis.² For diabetic patients at the end stage of KOA, they would receive total knee arthroplasty (TKA) to improve their quality of life.³

Physical rehabilitation is a vital part of the treatment process after undergoing TKA.⁴ Its primary objective is to enhance functional outcomes and facilitate the patient’s successful reintegration into their significant activities.⁵ With the aging population, rising rates of obesity, and increasing prevalence of KOA, the number of TKAs has been steadily on the rise. Consequently, hospital stays have become shorter, and patients are discharged home at an earlier stage.^6,7 As a result, community physical therapists shoulder a more substantial workload in providing rehabilitation services to this particular patient group. In certain regions of Quebec, Canada, approximately 20% of the caseload of community physical therapists consists of individuals who have undergone TKA. Moreover, these patients account for over 33% of home visits. However, owing to the ever-growing demand, community rehabilitation resources often struggle to meet these requirements effectively.^8,9 Therefore, it becomes crucial to explore new and effective approaches that ensure the delivery of appropriate and accessible care for this population.

Telerehabilitation offers a novel method for providing rehabilitation services remotely through information and telecommunication technologies. It stands as a viable alternative or supplement to traditional in-person visits, especially when accessing health care professionals is difficult. The last decade has seen an increase in research supporting telerehabilitation’s effectiveness post-TKA, though recent systematic reviews call for more extensive, controlled studies for a deeper evaluation of its clinical advantages.^10–12 In 2018, a systematic review¹³ and a meta-analysis were conducted to assess the efficacy of telerehabilitation for patients after TKA compared with face-to-face rehabilitation. Their findings affirmed that telerehabilitation could achieve comparable pain relief, better patient-reported outcome measures (PROMs) improvement, and better functional outcome, such as higher extension range and quadriceps strength. The incorporation of posture monitoring and correction technologies, which provide accurate and reliable feedback, can significantly enhance current rehabilitation practices.^14,15 Ideally, these technologies should offer continuous feedback for individuals with lower proficiency levels while using fading frequency schedules for more advanced users.¹⁴ Broadly speaking, there are five main types of monitoring methods available:

Traditional mechanical systems, such as goniometers.

Optical motion recognition technologies.¹⁶

Marker-less off-body tracking systems, including depth camera-based movement detection systems like Microsoft Kinect.¹⁷

Robot-based solutions.¹⁸

Wearable inertial measurement unit (IMU) sensor–based systems.¹⁹

In recent times, advancements in device miniaturization, sensing technologies, and body area networks have led to the rise of wearable rehabilitation technology.^20,21 This technology holds several advantages over traditional rehabilitation services,^22,23 including lower cost, flexibility in application, remote monitoring capabilities, and enhanced comfort. Wearable sensing systems offer the potential for independent training, serving as active monitoring systems that provide feedback to the end user. They may even facilitate telerehabilitation, enabling remote delivery of rehabilitation services.

The goal of this prospective study was to examine the effectiveness of wearable IMU sensor–based systems in telerehabilitation for diabetic patients after TKA.

Materials and Methods

Study design

This study, conducted at Shanghai Sixth People’s Hospital affiliated to Shanghai Jiao Tong University School of Medicine, utilized a single-center, randomized clinical trial design to evaluate the efficacy of telerehabilitation with and without IMU sensor assistance in patients with diabetes and KOA undergoing TKA. Leveraging the expertise from its Shanghai Clinical Center for Diabetes and the National Center for Orthopedic Surgery, the hospital offers an integrated care model for patients with concurrent diabetes and KOA. Participants, post-primary TKA, were randomized into two groups: a telerehabilitation group (TELE group) and an IMU-assisted telerehabilitation group (IMU group), both receiving identical rehabilitation interventions via in-home telerehabilitation over the first 6 weeks post-discharge. Evaluations were conducted at four time points—pre-surgery (E1), at hospital discharge (E2, 1 week post-surgery), post-intervention (E3, 8 weeks post-surgery), and follow-up (E4, 24 weeks post-surgery)—by independent evaluators blinded to group assignments.

Participants

Participants were enrolled from the surgical waiting lists at Shanghai Clinical Center for Diabetes and were referred to the orthopedic surgery department. To be eligible for inclusion, participants had to meet the following criteria: (1) awaiting a primary TKA following a diagnosis of KOA, (2) getting discharged from the hospital and returning home, (3) residing in an area with access to high-speed internet services (with a minimum upload speed of 512 kb/s), and (4) living within a 1 h driving distance from the treating hospital. Exclusion criteria included (1) having health conditions that could interfere with the tests or rehabilitation program, including undergoing other lower-limb surgeries within the past nine months; (2) planning a second lower-limb surgery within the next four months; (3) experiencing cognitive or collaboration problems; (4) encountering major postoperative complications; or (5) having weight-bearing restrictions lasting longer than 2 weeks after surgery.

Written informed consent of all patients was acquired in compliance with the Declaration of Helsinki before being included in the study. All study protocols were approved by the Research Ethics Committee and were registered at the Chinese clinical trial registry.

Intervention

The rehabilitation regimen encompassed a series of 18 sessions, each extending between 45 and 60 min, under the vigilant supervision of a certified physical therapist. To maintain uniformity in the approach, each therapist was assigned to exclusively monitor a single group. The intensity and duration of the intervention were meticulously aligned with the standardized protocols delineated by an expert panel. The intervention comprised multiple facets, initiating with a comprehensive evaluation conducted both before and after the exercise regimen (Fig. 1).

FIG. 1.

Trial profile of TELE group vs. IMU group in participants. IMU, inertial measurement unit.

The exercises’ intensity and difficulty level were individually tailored to each patient, taking into account their tolerance and specific needs. This approach ensured that each participant received optimal care and support throughout their rehabilitation journey.

Modes of service delivery

The rehabilitation program was delivered through a smartphone application (Joymotion^® software, Shanghai Medmotion Medical Management Co., Ltd., Shanghai, China) that provided participants with exercise instructions, feedback on their training performance, and the real-time, two-way video and audio interaction with the physical therapist (PT).

Patients in the IMU group were asked to wear an inertial sensor system developed by the researchers, as described by Nüesch et al.²⁴ For each participant, three inertial sensors were placed on the frontal shin and frontal thigh and on the pelvis, overlying the navel using elastic straps. Each sensor (dimensions: 60 mm × 15 mm × 35 mm) contained a triaxial accelerometer, gyroscope, and magnetometer (magnetometer data were not used in proprietary software computations). The system and model were first calibrated with the participant standing in a neutral upright position for 3 sec and then alternately lifting their legs and flexing their trunk forward. The spatiotemporal and kinematic time series of the hip and knee were computed using the manufacturer’s proprietary software.

Outcome measures

The primary outcome was the Knee Injury and Osteoarthritis Outcome Score (KOOS).^25,26 This questionnaire is widely used to evaluate the effect of intervention after TKA, and its metrological properties are well recognized.

Secondary outcomes include percentile values for 1 repetition maximum (1RM) machine leg press and the 30-sec chair sit-to-stand test (30 s-CST) for muscle strength and function measurement.²⁷ The 1RM machine leg press strength was assessed based on the procedures outlined by the National Strength and Conditioning Association²⁸ using a MED leg press (Technogym, Bracknell, United Kingdom) (Supplementary Data). The results were converted into percentile values for 1RM (percentile value = absolute 1RM/body mass).

The 30 s-CST is the American College of Sports Medicine–recommended function and strength measurement test for elderly individuals. Participants were asked to stand from the chair with a straight back without armrests and sit down as many times as possible in 30 sec.²⁹

The demographic and clinical characteristics of the patients, including their comorbidities, were documented at baseline. Cointerventions, health complications, adverse events, and level of physical activity were documented at each follow-up visit.

Sample size calculation

The primary outcome measure of KOOS was used to calculate the required sample size using “Sample Size Calculation-Continuous End-point” Integrated Platform for Designing Clinical Trials, V.2.0.2.³⁰ The minimal clinically important difference was defined as a 10-point increase for the KOOS.^31,32 The sample size was calculated for the test of equivalence of the groups at 90% power and an alpha level of 0.05 with equivalence margins of ±10 KOOS points, assuming a mean difference of 0 and a common standard deviation (SD) of 15 points. Forty-nine subjects for each group were required. To account for dropout rate up to a 20%, a total of 123 participants were required.

Randomization

A computer-generated randomization list (SAS Proc Plan, SAS/STAT 9.3, SAS Institute, Cary, NC) was prepared by the statistician and given to the study’s clinical coordinator of each site in a series of sealed envelopes. Afterward, the study coordinator proceeded to randomization in the patient’s presence.

Blinding

All evaluators and investigators were blinded to group assignment for the entire duration of the study. Decisions related to data analysis were taken, although investigators were still unaware of the group assignment. However, blinding subjects and clinicians was not possible, considering the nature of the intervention.

Statistical analysis

All patients’ data were coded and stored on the electronic data capture system. The system was operated within the hospital’s local servers. The primary analysis was performed in the per protocol population and the adverse events intention-to-treat (ITT) population. All statistical analyses were performed on SPSS Statistics version 24.0 (IBM Corp, Chicago, IL) and R version 4.3.2 (R Foundation for Statistical Computing, Vienna, Austria). Baseline data were presented as mean ± SD with 95% confidence intervals (CIs) unless stated otherwise. Intergroup differences in patients’ baseline characteristics were assessed using independent sample t-tests for continuous dependent variables and Fisher’s exact test for categorical data.

After treatment, continuous outcomes, including each questionnaire score and functional tests, were analyzed as change from baseline (e.g., E4 change from E1 = value of E4 value of E1). The outcomes were compared between intervention groups per time point in a linear mixed model for repeated measures (nlme, version 3.1–163, in R) as change from baseline (change from E1). The model included the interaction between time and intervention for all time points change from baseline and the factors of age and sex as fixed effects and participants as random effects.

Based on this model, differences between least squares means of groups were estimated per time point (emmeans, version 1.9.0, in R). Results of each time point were reported as least squares means (LSMeans) and standard errors (SEs) and differences between groups as LSMean with two-sided 95% CIs.

Results

Demographic characteristics

A total of 131 patients randomized after surgery, and 116 completed the rehabilitation and follow-up program. Demographic data are specified in Table 1. Patients of both groups had similar baseline characteristics (Table 1).

Table 1.

Baseline Characteristics of the Patients

Characteristic	TELE group (n = 57)	IMU group (n = 59)	p-value
Age (yr)	60.4 ± 3.0	61.1 ± 3.6	0.288
Male patients (no. [%])	31 (54.4)	33 (55.9)	0.867
BMI (kg/m²)	29.6 ± 1.4	29.2 ± 1.7	0.141
Duration of diabetes (yr)	11.7 ± 3.2	12.1 ± 3.1	0.474
HbA1c before surgery (%)	8.4 ± 0.7	8.3 ± 0.7	0.217
Occupation (no. [%])
Manual worker	5 (8.8)	8 (13.5)	0.804
Retired	45 (79.0)	45 (76.3)
Other	7 (12.2)	6 (10.2)
Living alone (no. [%])	4 (7.0)	4 (6.8)	1
Diabetes mellitus type (no. [%])			1
Type 1	6 (10.5)	7 (11.9)
Type 2	51 (89.5)	52 (88.1)
Comorbidity (no. [%])
Osteoporosis	11 (18.6)	8 (13.6)	0.252
COPD, ARDS, or emphysema	6 (10.5)	7 (11.9)	0.888
Congestive heart failure	2 (3.5)	1 (1.7)	0.378
Peripheral vascular disorder	3 (5.3)	5 (8.5)	0.414
Hypertension	17 (29.8)	19 (32.2)	0.862
Visual impairment	21 (36.8)	18 (30.5)	0.183
Hearing impairment	3 (5.3)	4 (6.8)	0.585
Baseline functional status
KOOS
Pain	42.3 ± 2.7	42.3 ± 2.1	0.993
Symptoms	52.1 ± 3.0	52.1 ± 2.5	0.99
ADL	49.4 ± 3.5	49.6 ± 3.3	0.805
Sport/Rec	16.3 ± 2.2	16.0 ± 1.8	0.396
QoL	26.4 ± 2.6	26.7 ± 2.6	0.594
1RM strength (kg/kg)	1.0 ± 0.1	1.0 ± 0.1	0.182
30s-CST (repetitions)	12.1 ± 1.0	11.9 ± 0.9	0.236
ROM (°)	125.0 ± 3.1	125.0 ± 2.6	0.968

Values are mean ± SD unless otherwise stated.

30 s-CST, 30-sec Chair Sit-to-Stand Test; ADL, activities of daily living; ARDS, acute respiratory distress syndrome; BMI, body mass index; COPD, chronic obstructive pulmonary disease; HbA1c, hemoglobin A1C; IMU, inertial measurement unit; KOOS, Knee Injury and Osteoarthritis Outcome Score; QoL, quality of life; RM, repetition maximum; ROM, range of motion; SD, standard deviation; Sport/Rec, sports and recreation.

Physical function

KOOS scores, range of motion (ROM), 1RM strength, and 30 s-CST at baseline were similar at both groups. At the last 24-week follow-up, KOOS gains adjusted for baseline values had significant difference in most subscales except for symptoms, especially for the subscales of activities of daily living and sports and recreational activities. Functional and strength tests that were also found had significant difference between two groups, except ROM. At the 8-week follow-up, the mean differences of the groups with respect to the subscales of activities of daily living, sports and recreational activities, and quality of life of KOOS gains adjusted for baseline values had significant difference. ROM, 1RM strength, and 30 s-CST were similar between the two groups at the 8-week follow-up (Table 2). The trajectories of the KOOS subscales are illustrated in Figure 2.

FIG. 2.

Trajectories of each KOOS subscale in the PP population. High values represent better outcome. Data points represent means at each follow-up time point; error bars, SD. * stands for the p-value of change from E1 estimated by the linear mixed model for repeated measures: *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001. IMU, inertial measurement unit; KOOS, Knee Injury and Osteoarthritis Outcome Score; PP, per protocol; QoL, quality of life; SD, standard deviation.

Table 2.

Adjusted Mean KOOS Scores and Functional Outcomes at Week 24 and Week 8 in the PP Population

	TELE (n = 57)	IMU (n = 59)	Estimated difference
	LSMean (SE)	LSMean (SE)	ΔLSMean (95% CI)	p-value
24-week follow-up
Change in KOOS score
Pain	22.3 ± 0.5	24.2 ± 0.5	1.9 (0.5–3.3)	0.0081
Symptoms	26.6 ± 0.7	27.3 ± 0.7	0.7 (−1.4–2.8)	0.4723
ADL	29.9 ± 0.6	36.7 ± 0.6	6.8 (5.1–9.5)	<0.0001
Sport/Rec	14.9 ± 0.4	22.9 ± 0.4	8.0 (6.8–9.2)	<0.0001
QoL	15.4 ± 0.6	19.0 ± 0.6	3.6 (2.0–5.2)	<.0001
Change in functional test
1RM strength (kg/kg)	1.21 ± 0.09	1.34 ± 0.16	0.13 (0.0–0.24)	0.0047
30 s-CST (repetitions)	3.6 ± 0.5	5.4 ± 0.5	1.8 (0.5–3.1)	0.0106
ROM (°)	12.0 ± 3.2	11.8 ± 3.1	−0.2 (−9.0–8.6)	0.9652
8-week follow-up
Change in KOOS score
Pain	−4.6 ± 0.3	−4.0 ± 0.3	0.6 (−0.3–1.5)	0.2136
Symptoms	−6.2 ± 0.5	−6.2 ± 0.5	0.0 (−1.3–1.3)	0.9225
ADL	−5.3 ± 0.4	−3.2 ± 0.4	2.1 (1.0 to −3.2)	0.0002
Sport/Rec	−4.5 ± 0.3	−2.7 ± 0.3	1.8 (1.0–2.6)	<0.0001
QoL	−0.6 ± 0.4	2.2 ± 0.4	2.8 (1.8–3.8)	<0.0001
Change in functional test
1RM strength (kg/kg)	0.28 ± 0.19	0.43 ± 0.18	0.15 (0.08–0.22)	0.1737
30 s-CST (repetitions)	−4.8 ± 0.3	−4.3 ± 0.3	0.5 (−0.3–1.3)	0.2411
ROM (°)	−36.8 ± 2.0	−36.3 ± 2.0	0.5 (−5.0–6.0)	0.8756

Values are LSMean ± SE unless otherwise stated.

30 s-CST, 30-sec Chair Sit-to-Stand Test; ADL, activities of daily living; IMU, inertial measurement unit; KOOS, Knee Injury and Osteoarthritis Outcome Score; LSMean, least squares mean; QoL, quality of life; RM, repetition maximum; ROM, range of motion; SE, standard error; Sport/Rec, sports and recreation.

Adverse events, loss to follow-up

During the follow-up period, a similar proportion of participants in both groups reported adverse events. No serious event was related to the telerehabilitation intervention, whereas one minor event was possibly related to the standard intervention (Table 3). The proportions lost to follow-up were equivalent in both groups. Most occurred at the last follow-up survey (Fig. 1).

Table 3.

Adverse Events and Serious Adverse Events

Adverse events	TELE group (n = 65)	IMU group (n = 66)
Patients with adverse events (no. [%])	7 (10.8)	8 (12.1)
Events related to study therapy (no.)	1^#	0
Events unrelated to study therapy (no.)	9	10
Type of event (no.)
Involved knee
Pain	3	2
Bruising	1	0
Swelling	1	0
Signs of infection (swelling, redness, heat, or pus)	1	1
Problems with wound-healing	1	1
Mobilization under anesthesia	0	0
Other
Fall with minor symptoms	1	3
Nausea and dizziness	0	1
Back pain	1	2
Anxiety about knee recovery	1	0

Serious adverse events
Patients with serious adverse events (no. [%])	8 (12.3)	5 (7.6)
Events related to study therapy (no.)	0	0
Events unrelated to study therapy (no.)	8	5
Type of event (no.)
Death	1	0
Hospitalization	3	2
Degradation of the general condition	2^*	0
Hip fracture because of fall	1^*	0
Gastrointestinal disorder	0	1
Cardiac arrhythmia	1	0
Spinal surgery	0	1^*
Total knee arthroplasty on contralateral side	0	1^*

One patient fell during intervention with minor consequent symptoms.

Events related to hospitalization.

IMU, inertial measurement unit.

Discussion

In this study, we assessed the efficacy of a portable and wearable IMU software and hardware platform for the telerehabilitation of post-TKA patients. To our knowledge, this clinical trial is the inaugural investigation demonstrating the feasibility and practical application of such a portable and wearable IMU platform in a real-world community setting for telerehabilitation post-TKA. The system’s utility was confirmed through PROMs and additional functional assessments, including 1RM strength tests and the 30 s-CST.

A recent expert consensus panel in North America recommended that post-acute rehabilitation following TKA should include supervised interventions conducted by trained health care professionals shortly after hospital discharge.³³ The panel also recommended individual therapy, rather than group therapy, either in an outpatient setting or at home, considering the variations in rehabilitation practices worldwide.^33,34 In some countries, like Canada, Australia, and the United States, about one-third of patients receive rehabilitation through face-to-face home-care services.^9,33,35,36 With advancements in communication technologies, remote supervision has emerged as a feasible alternative, provided it delivers comparable outcomes. In our study, the sensor system is designed to achieve three primary objectives: (1) It facilitates patient assessments and aids physical therapists in delivering effective care. (2) The wearable IMU system offers significant societal advantages by overcoming the limitations of time and space that allow patients to easily use the system at home. This approach not only diminishes health care costs but also alleviates the strain on hospital resources. (3) As outlined in this article, our ambition for this system encompasses its capability for ongoing monitoring and assessment of various conditions, including TKA, anterior cruciate ligament injuries, and fractures. In addition, it paves the way for the creation of active rehabilitation programs that enable patients to restore knee functionality from the comfort of their own homes.

The interactive systems of wearable sensor-based technology have been developed for decades and were proved to be effective for telerehabilitation.³⁷ Wang et al. proposed a taxonomy that consists of three dimensions: measurement, sensing technology, and feedback.³⁷ The IMU sensors deployed in this study primarily gather data on the patients’ limb positions, synchronizing this information with the software to generate simulated imagery. This process facilitates interactive feedback by comparing these simulated images with instructional ones. Such visualization effectively mirrors the user’s body movements in real time, juxtaposed with a virtual three-dimensional model. This approach can improve learning through imitation.³⁸ Moreover, users can enhance motor learning through mental rehearsal, stimulating brain regions analogous to those engaged in actual physical movements.³⁹ Recent advancements in smartphone technology, characterized by their widespread accessibility, compactness, advanced processing power, and the integration of various sensors and displays, have markedly influenced their utilization in rehabilitation systems. The usage of smartphones for providing feedback, such as visual cues, has become increasingly prevalent and effective, especially in frameworks designed for remote monitoring. Yet, studies focusing on the application of wearable sensor technology in remote rehabilitation remain absent. Our findings illustrate the practicality and certain benefits of using wearable sensor technology in routine procedures like TKA. Building upon existing literature, we contend that wearable sensor technology can refine rehabilitation exercises by improving visual feedback for patients. Moreover, we propose that visual feedback involving standardized movements could enhance patient adherence and reduce anxiety. Future research is necessary to assess the impact of improved patient compliance and reduced anxiety through interactive systems based on wearable sensing technology in telerehabilitation.

Implications for IMU sensor-based feedback assistance in telerehabilitation

The incorporation of IMU sensor-based feedback in telerehabilitation for orthopedic conditions carries profound implications across various aspects of patient care and system efficiency. The immediate feedback provided by IMU sensors plays a pivotal role in enhancing patient engagement and compliance. It encourages patients to perform exercises with greater precision and consistency, which are critical components of successful orthopedic recovery. Moreover, the capability for health care professionals to monitor patient progress remotely and tailor rehabilitation programs to individual needs significantly boosts recovery outcomes. The wealth of data generated by IMU sensors offers health care providers critical insights into patients’ rehabilitation processes. This data-driven approach enables more informed decision-making regarding treatment plans, ensuring that interventions are based on concrete evidence of what works best for the patient. From a cost perspective, IMU sensor-based rehabilitation demonstrates significant savings by reducing the necessity for frequent in-person visits. This not only lowers expenses for health care systems and patients but also makes rehabilitation services more accessible, particularly for individuals in remote or underserved areas who might otherwise face barriers to receiving quality care. This symbiosis of technology and health care promises to revolutionize the field of orthopedic telerehabilitation, making it more effective, efficient, and accessible.

Study strengths and limitations

Our study has both strengths and limitations. The study was performed in terms of telerehabilitation versus a sensor-assisted version, and it was possible to blind the investigators performing the statistical evaluation. Other strengths of the study were high adherence with regard to the number of treatments, valid and reliable outcome measures, and robust statistical methods. In addition, the study underscores the critical importance of technological literacy for users, the accuracy of the devices used, and the paramount importance of safeguarding patient privacy and data security. These considerations highlight significant areas for future research and development in the field of remote rehabilitation technologies.

This study’s limitations are as follows. First, it’s essential to also acknowledge the study’s design, being confined to a single center. This design choice restricts the diversity of the patient population and the generalizability of the findings. Second, the research did not evaluate patient compliance or their mental state, which leaves unanswered questions regarding how wearable sensors and visual feedback might affect these areas. Third, the absence of a face-to face rehabilitation group utilizing wearable sensors makes it difficult to distinctly attribute improvements in remote rehabilitation to the wearable technology itself. This study’s focus on obese patients who have undergone TKA also narrows its applicability, potentially limiting the relevance of its findings to broader patient groups and conditions. Moreover, there is a pressing need for further investigation into whether the costs associated with implementing wearable technology are outweighed by enhancements in rehabilitation outcomes.

Conclusion

Overall, IMU sensor-based feedback assistance holds great promise for revolutionizing the field of telerehabilitation in orthopedics, making it more efficient, accessible, and tailored to individual patient needs.

Footnotes

Data Availability Statement

The data analyzed in this study are subject to the following licenses/restrictions: contact the corresponding author. Requests to access these datasets should be directed to lushengdi@shsmu.edu.cn.

Ethics Statement

The studies involving humans were approved by the Research Ethics Committee of Shanghai Sixth People’s Hospital affiliated to Shanghai Jiao Tong University School of Medicine (Approval No.:2022-045-[1]). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

This trial was registered at the Chinese clinical trial registry. Its Standard Randomized Controlled Trial Number is ChiCTR2100053313.

Author Contributions

R.Z.: Writing—original draft (equal), Software (lead), Methodology (supporting), Formal analysis (lead). T.W.: Writing—original draft (equal), Methodology (lead), Data curation (lead). L.H.: Writing—original draft (supporting), Investigation (lead), Methodology (supporting). H.W.: Writing—review and editing (equal), Supervision (lead), Validation (lead). S.L.: Writing—review and editing (equal), Conceptualization (lead), Project administration (lead). All authors contributed to the article and approved the submitted version.

Author Disclosure Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding Information

The authors declare that no financial support was received for the research, authorship, and/or publication of this article.

Supplementary Material

Abbreviations Used

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Effectiveness of Inertial Measurement Unit Sensor–Based Feedback Assistance in Telerehabilitation of Patients with Diabetes after Total Knee Arthroplasty: A Randomized Controlled Trial

Abstract

Background:

Method:

Results:

Conclusion:

Trial Registration:

Introduction

Materials and Methods

Study design

Participants

Intervention

Modes of service delivery

Outcome measures

Sample size calculation

Randomization

Blinding

Statistical analysis

Results

Demographic characteristics

Physical function

Adverse events, loss to follow-up

Discussion

Implications for IMU sensor-based feedback assistance in telerehabilitation

Study strengths and limitations

Conclusion

Footnotes

Data Availability Statement

Ethics Statement

Author Contributions

Author Disclosure Statement

Funding Information

Supplementary Material

Abbreviations Used

References

Supplementary Material