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| Name | Class |
|---|---|
| Aalborg University | OTHER |
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The goal of this observational study is to investigate the potential of wearable sensors for monitoring the postoperative recovery of patients after TKA.
The main question the study aims to answer is:
• whether alterations in gait characteristics and the changes in PA levels measured by wearable PA trackers can accurately reflect a patient's postoperative recovery status and provide clinically relevant information to aid their management.
Participants will wear PA trackers during the perioperative period of TKA (2 weeks before until 3 months after, and then agin for 2 weeks one year after the surgery) and we will analyze their gait and PA and correlate them with their recovery after surgery.
Introduction
Total knee arthroplasty (TKA) is a common surgical intervention for end-stage knee osteoarthritis (OA) patients who have failed conservative treatments. However, despite advances in surgical techniques and postoperative care, some patients experience complications and delayed recovery, leading to increased healthcare costs and worse clinical outcomes. Therefore, monitoring the postoperative recovery of TKA patients is critical for improving the outcomes and reducing healthcare costs.
Currently, various approaches assess patients' postoperative progress following TKA, including patient- and clinician-reported outcomes, as well as radiographic imaging. However, these methods are typically conducted at limited time intervals, and their objectivity is frequently questioned due to the potential for variability. The physical activity (PA) of patients, particularly walking as the primary form of PA, has proven to be a reliable indicator of their overall health and functionality. Abnormalities in walking patterns or reduced levels of physical activity can indicate decreased compliance, pain, or the occurrence of adverse events. Early diagnosis and treatment are crucial in mitigating the potential consequences of these adverse events, such as pulmonary embolism (PE), deep vein thrombosis (DVT), infection, and others.
In recent years, wearable sensors, such as PA trackers, have emerged as a promising tool for monitoring postoperative recovery. These sensors can continuously and objectively monitor a patient's PA levels and provide data that can be used to track the patients' recovery progress based on their daily activities. Furthermore, studies have demonstrated that patients are able to comply with the use of wearable sensors during the postoperative period after orthopedic surgeries. The investigators have demonstrated the patients' compliance with using the same sensors in a separate study. However, despite the potential benefits of wearable sensors for monitoring TKA recovery, several uncertainties remain. One key issue is whether changes in PA levels and gait alterations detected by wearable sensors can provide a reliable indicator of a patient's postoperative recovery status. Moreover, it is currently unclear whether the data collected from these sensors can yield clinically meaningful information that can effectively aid in managing TKA patients. Further research is needed to address these questions and establish the potential value of wearable sensors in the context of monitoring recovery after TKA.
Therefore, this study aims to investigate the potential of wearable sensors, specifically PA trackers, for monitoring the postoperative recovery of patients after TKA. Our study will recruit patients with knee OA scheduled to undergo TKA and monitor them continuously for three months after surgery using PA trackers. By analyzing the data collected from these trackers, the investigators aim to determine whether alterations in gait characteristics and the changes in PA levels measured by wearable PA trackers can accurately reflect a patient's postoperative recovery status and provide clinically relevant information to aid their management. In addition, in the study the investigators will measure PA and gait accelerations one year after TKA to evaluate the final outcome of the surgery once the recovery period is complete.
Methods
The investigators will use PA trackers to monitor the participants' PA levels. The PA trackers are previously validated sensors (SENS Motion®) designed for monitoring PA in health care and comprise accelerometers. Plasters on the lateral distal thigh will attach the sensors and measure 3D linear accelerations of lower limbs. The linear accelerations will be translated into the amount of PA and the number of steps taken per day by the SENS Motion algorithm.
The investigators will monitor the participants 24/7 using PA trackers from two weeks before surgery until three months after surgery. A two-weeks follow-up measurement will also be performed 12 months after surgery. The study will consist of three data collection periods: pre-op (two weeks), post-op (three months), and 12-months follow-up (two weeks).
A. Enrollment (Baseline visit)
B. Pre-op period
C. Operation day
• On the day of the operation, the operation nurse will remove the sensors before surgery and reattach them in the exact location following the procedure. The patient's records will be reviewed to obtain information on the type and duration of surgery, as well as any complications that may have occurred. The data will be recorded in a REDCap database.
D. Post-op Period
E. 12-months follow up
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Measuring gait | Other | Measurements are performed by PA trackers. The measurements include:
|
|
| Measure | Description | Time Frame |
|---|---|---|
| The postoperative daily changes from the baseline (preoperative) Fourier coefficients of walking acceleration signals until 3 months after total knee arthroplasty | 3D lower limb accelerations corresponding to walking bouts will be extracted and transformed by Fast Fourier Transform. The first five pairs of Fourier coefficients will be used for data analysis. | Two weeks before the surgery until three months after the surgery |
| The postoperative changes from the baseline (preoperative) Fourier coefficients of walking acceleration signals at 12 months after total knee arthroplasty | 3D lower limb accelerations corresponding to walking bouts will be extracted and transformed by Fast Fourier Transform. The first five pairs of Fourier coefficients will be used for data analysis. | Two weeks before and 12 months after the surgery |
| Measure | Description | Time Frame |
|---|---|---|
| The postoperative daily changes from the baseline (preoperative) cadence until 3 months after total knee arthroplasty | Acceleration data will be analyzed to determine the gait cadence defined as the number of steps taken per minute. | Two weeks before the surgery until three months after the surgery |
| The postoperative daily changes from the baseline (preoperative) stance time until 3 months after total knee arthroplasty |
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Inclusion Criteria:
Exclusion Criteria:
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All eligible patients who are scheduled for TKA from a certain date onwards will be screened and offered participation in the study.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Arash Ghaffari, MD | Contact | +45-91483966 | a.ghaffari@rn.dk |
| Name | Affiliation | Role |
|---|---|---|
| Ole Rahbek, MD, PhD | Aalborg University Hospital | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Regionshospital Nordjylland, Frederikshavn | Recruiting | Frederikshavn | North Denmark | 9900 | Denmark |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 28690473 | Background | Steinhaus ME, Christ AB, Cross MB. Total Knee Arthroplasty for Knee Osteoarthritis: Support for a Foregone Conclusion? HSS J. 2017 Jul;13(2):207-210. doi: 10.1007/s11420-017-9558-4. Epub 2017 May 16. | |
| 29416347 | Background | Feng JE, Novikov D, Anoushiravani AA, Schwarzkopf R. Total knee arthroplasty: improving outcomes with a multidisciplinary approach. J Multidiscip Healthc. 2018 Jan 25;11:63-73. doi: 10.2147/JMDH.S140550. eCollection 2018. |
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Acceleration data will be analyzed to determine the stance time of the gait described as the percentage of the gait cycle time spent on stance phase. |
| Two weeks before the surgery until three months after the surgery |
| The postoperative daily changes from the baseline (preoperative) rest time until 3 months after total knee arthroplasty | Acceleration data will be analyzed to determine the proportion of time the patient spent lying down during each day expressed as the percentage. | Two weeks before the surgery until three months after the surgery |
| The postoperative daily changes from the baseline (preoperative) sitting time until 3 months after total knee arthroplasty | Acceleration data will be analyzed to determine the proportion of time the patient spent sitting during each day expressed as the percentage. | Two weeks before the surgery until three months after the surgery |
| The postoperative daily changes from the baseline (preoperative) standing time until 3 months after total knee arthroplasty | Acceleration data will be analyzed to determine the proportion of time the patient spent standing during each day expressed as the percentage. | Two weeks before the surgery until three months after the surgery |
| The postoperative daily changes from the baseline (preoperative) walking time until 3 months after total knee arthroplasty | Acceleration data will be analyzed to determine the proportion of time the patient spent walking during each day expressed as the percentage. | Two weeks before the surgery until three months after the surgery |
| The postoperative daily changes from the baseline (preoperative) step counts until 3 months after total knee arthroplasty | Acceleration data will be analyzed to determine the number of steps the patient takes during each day. | Two weeks before the surgery until three months after the surgery |
| The postoperative daily changes from the baseline (preoperative) sit-to-stand counts until 3 months after total knee arthroplasty | Acceleration data will be analyzed to determine the number of sit-to-stands the patient performs during each day. | Two weeks before the surgery until three months after the surgery |
| The postoperative daily changes from the baseline (preoperative) activity count index until 3 months after total knee arthroplasty | The activity count index will be calculated by using the variance of the magnitude of the linear accelerations and demonstrates the quantitative level of PA. | Two weeks before the surgery until three months after the surgery |
| The postoperative changes from the baseline (preoperative) cadence at 12 months after total knee arthroplasty | Acceleration data will be analyzed to determine the gait cadence defined as the number of steps taken per minute. | Two weeks before and 12 months after the surgery |
| The postoperative changes from the baseline (preoperative) stance time at 12 months after total knee arthroplasty | Acceleration data will be analyzed to determine the stance time of the gait described as the percentage of the gait cycle time spent on stance phase. | Two weeks before and 12 months after the surgery |
| The postoperative changes from the baseline (preoperative) rest time at 12 months after total knee arthroplasty | Acceleration data will be analyzed to determine the proportion of time the patient spent lying down during each day expressed as the percentage. | Two weeks before and 12 months after the surgery |
| The postoperative changes from the baseline (preoperative) sitting time at 12 months after total knee arthroplasty | Acceleration data will be analyzed to determine the proportion of time the patient spent sitting during each day expressed as the percentage. | Two weeks before and 12 months after the surgery |
| The postoperative changes from the baseline (preoperative) standing time at 12 months after total knee arthroplasty | Acceleration data will be analyzed to determine the proportion of time the patient spent standing during each day expressed as the percentage. | Two weeks before and 12 months after the surgery |
| The postoperative changes from the baseline (preoperative) walking time at 12 months after total knee arthroplasty | Acceleration data will be analyzed to determine the proportion of time the patient spent walking during each day expressed as the percentage. | Two weeks before and 12 months after the surgery |
| The postoperative changes from the baseline (preoperative) step counts at 12 months after total knee arthroplasty | Acceleration data will be analyzed to determine the number of steps the patient takes during each day. | Two weeks before and 12 months after the surgery |
| The postoperative changes from the baseline (preoperative) sit-to-stand counts at 12 months after total knee arthroplasty | Acceleration data will be analyzed to determine the number of sit-to-stands the patient performs during each day. | Two weeks before and 12 months after the surgery |
| The postoperative changes from the baseline (preoperative) activity count index at 12 months after total knee arthroplasty | The activity count index will be calculated by using the variance of the magnitude of the linear accelerations and demonstrates the quantitative level of PA. | Two weeks before and 12 months after the surgery |
| The change in Knee injury and Osteoarthritis Outcome Score (KOOS) | The KOOS questionnaire is an instrument to assess the patient's opinion about their knee and associated problems. Only the domains evaluating the pain, symptoms and functions of daily activities will be used. | Baseline (2 weeks before surgery); and 1 month, 2 months, 3 months, and 12 months after the surgery. |
| The change in EuroQol-5 Dimensions-3 Levels (EQ-5D-3L) questionnaire | EQ-5D-3L is a generic tool for Patient Reported Outcomes (PRO) measurement that can assess patients' quality of life, irrespective of the disease. | Baseline (2 weeks before surgery); and 1 month, 2 months, 3 months, and 12 months after the surgery. |
| 34372377 | Background | Bolam SM, Batinica B, Yeung TC, Weaver S, Cantamessa A, Vanderboor TC, Yeung S, Munro JT, Fernandez JW, Besier TF, Monk AP. Remote Patient Monitoring with Wearable Sensors Following Knee Arthroplasty. Sensors (Basel). 2021 Jul 29;21(15):5143. doi: 10.3390/s21155143. |
| 30911852 | Background | Tiwari V, Lee J, Sharma G, Kang YG, Kim TK. Temporal patterns of commonly used clinical outcome scales during a 5-year period after total knee arthroplasty. J Orthop Traumatol. 2019 Mar 25;20(1):16. doi: 10.1186/s10195-019-0520-8. |
| 16534088 | Background | Warburton DE, Nicol CW, Bredin SS. Health benefits of physical activity: the evidence. CMAJ. 2006 Mar 14;174(6):801-9. doi: 10.1503/cmaj.051351. |
| 27417610 | Background | Gordon R, Bloxham S. A Systematic Review of the Effects of Exercise and Physical Activity on Non-Specific Chronic Low Back Pain. Healthcare (Basel). 2016 Apr 25;4(2):22. doi: 10.3390/healthcare4020022. |
| 1560799 | Background | Carson JL, Kelley MA, Duff A, Weg JG, Fulkerson WJ, Palevsky HI, Schwartz JS, Thompson BT, Popovich J Jr, Hobbins TE, et al. The clinical course of pulmonary embolism. N Engl J Med. 1992 May 7;326(19):1240-5. doi: 10.1056/NEJM199205073261902. |
| 36633891 | Background | Iovanel G, Ayers D, Zheng H. The Role of Wearable Technology in Measuring and Supporting Patient Outcomes Following Total Joint Replacement: Review of the Literature. JMIR Perioper Med. 2023 Jan 12;6:e39396. doi: 10.2196/39396. |
| 32010456 | Background | Sliepen M, Lipperts M, Tjur M, Mechlenburg I. Use of accelerometer-based activity monitoring in orthopaedics: benefits, impact and practical considerations. EFORT Open Rev. 2020 Jan 28;4(12):678-685. doi: 10.1302/2058-5241.4.180041. eCollection 2019 Dec. |
| 32644435 | Background | Mendiratta P, Schoo C, Latif R. Clinical Frailty Scale. 2023 Apr 23. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2026 Jan-. Available from http://www.ncbi.nlm.nih.gov/books/NBK559009/ |
| 35890969 | Background | Ghaffari A, Rahbek O, Lauritsen REK, Kappel A, Kold S, Rasmussen J. Criterion Validity of Linear Accelerations Measured with Low-Sampling-Frequency Accelerometers during Overground Walking in Elderly Patients with Knee Osteoarthritis. Sensors (Basel). 2022 Jul 15;22(14):5289. doi: 10.3390/s22145289. |
| 9699158 | Background | Roos EM, Roos HP, Lohmander LS, Ekdahl C, Beynnon BD. Knee Injury and Osteoarthritis Outcome Score (KOOS)--development of a self-administered outcome measure. J Orthop Sports Phys Ther. 1998 Aug;28(2):88-96. doi: 10.2519/jospt.1998.28.2.88. |
| 11491192 | Background | Rabin R, de Charro F. EQ-5D: a measure of health status from the EuroQol Group. Ann Med. 2001 Jul;33(5):337-43. doi: 10.3109/07853890109002087. |
| 22810157 | Background | Healy WL, Della Valle CJ, Iorio R, Berend KR, Cushner FD, Dalury DF, Lonner JH. Complications of total knee arthroplasty: standardized list and definitions of the Knee Society. Clin Orthop Relat Res. 2013 Jan;471(1):215-20. doi: 10.1007/s11999-012-2489-y. |
| 40931323 | Derived | Ghaffari A, Clasen PD, Kappel A, Rasmussen J, Gurchiek RD, Kold S, Rahbek O. Monitoring Gait Recovery After Total Knee Arthroplasty Using Wearable Sensors: Responsiveness of Gait Accelerations. J Orthop Res. 2025 Dec;43(12):2165-2177. doi: 10.1002/jor.70058. Epub 2025 Sep 10. |