xml: <clinical_study>
    <!-- This xml conforms to an XML Schema at:
      https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
    <required_header>
      <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
      <link_text>Link to the current ClinicalTrials.gov record.</link_text>
      <url>https://clinicaltrials.gov/ct2/show/NCT05992064</url>
    </required_header>
    <id_info>
      <org_study_id>F2022-196</org_study_id>
      <nct_id>NCT05992064</nct_id>
    </id_info>
    <brief_title>Wearable Sensors for Monitoring Recovery After Total Knee Arthroplasty</brief_title>
    <official_title>Investigating the Potential of Wearable Sensors for Monitoring the Postoperative Recovery of Total Knee Arthroplasty Patients: A Protocol for Prospective Longitudinal Observational Study</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Aalborg University Hospital</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
      <collaborator>
        <agency>Aalborg University</agency>
        <agency_class>Other</agency_class>
      </collaborator>
    </sponsors>
    <source>Aalborg University Hospital</source>
    <oversight_info>
      <has_dmc>No</has_dmc>
      <is_fda_regulated_drug>No</is_fda_regulated_drug>
      <is_fda_regulated_device>No</is_fda_regulated_device>
    </oversight_info>
    <brief_summary>
      <textblock>
        The goal of this observational study is to investigate the potential of wearable sensors for&#xD;
        monitoring the postoperative recovery of patients after TKA.&#xD;
  &#xD;
        The main question the study aims to answer is:&#xD;
  &#xD;
        • whether alterations in gait characteristics and the changes in PA levels measured by&#xD;
        wearable PA trackers can accurately reflect a patient's postoperative recovery status and&#xD;
        provide clinically relevant information to aid their management.&#xD;
  &#xD;
        Participants will wear PA trackers during the perioperative period of TKA (2 weeks before&#xD;
        until 3 months after, and then agin for 2 weeks one year after the surgery) and we will&#xD;
        analyze their gait and PA and correlate them with their recovery after surgery.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        Introduction&#xD;
  &#xD;
        Total knee arthroplasty (TKA) is a common surgical intervention for end-stage knee&#xD;
        osteoarthritis (OA) patients who have failed conservative treatments. However, despite&#xD;
        advances in surgical techniques and postoperative care, some patients experience&#xD;
        complications and delayed recovery, leading to increased healthcare costs and worse clinical&#xD;
        outcomes. Therefore, monitoring the postoperative recovery of TKA patients is critical for&#xD;
        improving the outcomes and reducing healthcare costs.&#xD;
  &#xD;
        Currently, various approaches assess patients' postoperative progress following TKA,&#xD;
        including patient- and clinician-reported outcomes, as well as radiographic imaging. However,&#xD;
        these methods are typically conducted at limited time intervals, and their objectivity is&#xD;
        frequently questioned due to the potential for variability. The physical activity (PA) of&#xD;
        patients, particularly walking as the primary form of PA, has proven to be a reliable&#xD;
        indicator of their overall health and functionality. Abnormalities in walking patterns or&#xD;
        reduced levels of physical activity can indicate decreased compliance, pain, or the&#xD;
        occurrence of adverse events. Early diagnosis and treatment are crucial in mitigating the&#xD;
        potential consequences of these adverse events, such as pulmonary embolism (PE), deep vein&#xD;
        thrombosis (DVT), infection, and others.&#xD;
  &#xD;
        In recent years, wearable sensors, such as PA trackers, have emerged as a promising tool for&#xD;
        monitoring postoperative recovery. These sensors can continuously and objectively monitor a&#xD;
        patient's PA levels and provide data that can be used to track the patients' recovery&#xD;
        progress based on their daily activities. Furthermore, studies have demonstrated that&#xD;
        patients are able to comply with the use of wearable sensors during the postoperative period&#xD;
        after orthopedic surgeries. The investigators have demonstrated the patients' compliance with&#xD;
        using the same sensors in a separate study. However, despite the potential benefits of&#xD;
        wearable sensors for monitoring TKA recovery, several uncertainties remain. One key issue is&#xD;
        whether changes in PA levels and gait alterations detected by wearable sensors can provide a&#xD;
        reliable indicator of a patient's postoperative recovery status. Moreover, it is currently&#xD;
        unclear whether the data collected from these sensors can yield clinically meaningful&#xD;
        information that can effectively aid in managing TKA patients. Further research is needed to&#xD;
        address these questions and establish the potential value of wearable sensors in the context&#xD;
        of monitoring recovery after TKA.&#xD;
  &#xD;
        Therefore, this study aims to investigate the potential of wearable sensors, specifically PA&#xD;
        trackers, for monitoring the postoperative recovery of patients after TKA. Our study will&#xD;
        recruit patients with knee OA scheduled to undergo TKA and monitor them continuously for&#xD;
        three months after surgery using PA trackers. By analyzing the data collected from these&#xD;
        trackers, the investigators aim to determine whether alterations in gait characteristics and&#xD;
        the changes in PA levels measured by wearable PA trackers can accurately reflect a patient's&#xD;
        postoperative recovery status and provide clinically relevant information to aid their&#xD;
        management. In addition, in the study the investigators will measure PA and gait&#xD;
        accelerations one year after TKA to evaluate the final outcome of the surgery once the&#xD;
        recovery period is complete.&#xD;
  &#xD;
        Methods&#xD;
  &#xD;
        The investigators will use PA trackers to monitor the participants' PA levels. The PA&#xD;
        trackers are previously validated sensors (SENS Motion®) designed for monitoring PA in health&#xD;
        care and comprise accelerometers. Plasters on the lateral distal thigh will attach the&#xD;
        sensors and measure 3D linear accelerations of lower limbs. The linear accelerations will be&#xD;
        translated into the amount of PA and the number of steps taken per day by the SENS Motion&#xD;
        algorithm.&#xD;
  &#xD;
        The investigators will monitor the participants 24/7 using PA trackers from two weeks before&#xD;
        surgery until three months after surgery. A two-weeks follow-up measurement will also be&#xD;
        performed 12 months after surgery. The study will consist of three data collection periods:&#xD;
        pre-op (two weeks), post-op (three months), and 12-months follow-up (two weeks).&#xD;
  &#xD;
        A. Enrollment (Baseline visit)&#xD;
  &#xD;
          -  At least two weeks before the surgery, patients will be enrolled in the study and given&#xD;
             detailed information about the protocol. Informed consent will be obtained, and basic&#xD;
             patient information, including their civil registration number, age, sex, comorbidities,&#xD;
             surgical history, height, weight, and lower limb lengths, will be collected. In&#xD;
             addition, patients will be given knee injury and osteoarthritis outcome score (KOOS) and&#xD;
             European Quality of Life 5 Dimensions 3 Level Version (EQ-5D-3L) questionnaires to&#xD;
             complete. The investigators will also evaluate the patients' frailty level using CFS.&#xD;
             All data will be recorded in a REDCap database hosted by region of North Jutland,&#xD;
             Denmark.&#xD;
  &#xD;
          -  The location of the sensors on the distal thigh of the affected knee will be marked, and&#xD;
             a PA tracker (SENS Motion) will be attached to the specified location. Patients will&#xD;
             then be asked to walk at a self-selected speed for about two minutes to register a&#xD;
             template for their walking pattern. The SENS app will be installed on the patients'&#xD;
             smartphones. Next, the patients will be instructed on transferring data from sensors'&#xD;
             internal memory to the SENS cloud system, changing the adhesives, and attaching the&#xD;
             sensor to the exact location specified by the markers. Patients will be provided enough&#xD;
             plasters and a marker pen, and instructional material will guide them through the&#xD;
             process. Participants will be instructed to wear the devices at all times, even when&#xD;
             showering or swimming.&#xD;
  &#xD;
        B. Pre-op period&#xD;
  &#xD;
          -  The PA tracker will continuously record the patients' PA levels and lower limb&#xD;
             accelerations. The collected data will be transferred daily from the PA tracker to the&#xD;
             SENS cloud system.&#xD;
  &#xD;
          -  To ensure the accuracy and completeness of the data, the investigators will monitor the&#xD;
             data transfer process. Specifically, the data collected by the PA tracker will be&#xD;
             transferred from the SENS cloud system to the Q-drive every week. If a participant fails&#xD;
             to transfer their data within 72 hours, they will be contacted to remind them to do so.&#xD;
  &#xD;
          -  All data collected during pre-op will be used to establish the participants' baseline PA&#xD;
             levels. This information will be used as a reference point to assess changes in PA&#xD;
             levels and lower limb accelerations after surgery and during the follow-up period.&#xD;
  &#xD;
        C. Operation day&#xD;
  &#xD;
        • On the day of the operation, the operation nurse will remove the sensors before surgery and&#xD;
        reattach them in the exact location following the procedure. The patient's records will be&#xD;
        reviewed to obtain information on the type and duration of surgery, as well as any&#xD;
        complications that may have occurred. The data will be recorded in a REDCap database.&#xD;
  &#xD;
        D. Post-op Period&#xD;
  &#xD;
          -  After surgery, participants will be instructed to wear the tracker continuously for&#xD;
             three months. During the postoperative period, the investigators will closely monitor&#xD;
             patients' PA levels. Patients who fail to transfer sensor data within 72 hours will be&#xD;
             contacted to ensure continuous data collection. The sensor data will be transferred&#xD;
             weekly from the SENS cloud system to Q-drive. The investigators will also administer&#xD;
             weekly EQ-5D-3L questionnaires and monthly KOOS questionnaires to assess patients'&#xD;
             health status and knee function through the REDCap platform.&#xD;
  &#xD;
          -  After three months, the sensors will be turned off and returned to the hospital. The&#xD;
             investigators will extract relevant information from patients' records to identify&#xD;
             possible complications and classify them based on the standardized list and definitions&#xD;
             of the Knee Society 14.&#xD;
  &#xD;
        E. 12-months follow up&#xD;
  &#xD;
          -  The follow-up period will consist of an additional two weeks of data collection one year&#xD;
             after surgery, during which participants will continue to wear the tracker to assess the&#xD;
             changes in PA levels and lower limb accelerations.&#xD;
  &#xD;
          -  At the 12-month follow-up, patients will be invited to participate in the follow-up part&#xD;
             of the study, and if they consent, they will receive sensors to wear continuously for 14&#xD;
             days. Data will be collected continuously during this period, and KOOS and EQ-5D-3L&#xD;
             questionnaires will be administered through REDCap. In addition, the patients' journals&#xD;
             will be reviewed for any potential complications.&#xD;
  &#xD;
          -  Once the 14-day period is complete, the sensors will be deactivated and returned to the&#xD;
             hospital.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Not yet recruiting</overall_status>
    <start_date type="Anticipated">October 1, 2023</start_date>
    <completion_date type="Anticipated">October 1, 2025</completion_date>
    <primary_completion_date type="Anticipated">October 1, 2024</primary_completion_date>
    <study_type>Observational</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <observational_model>Cohort</observational_model>
      <time_perspective>Prospective</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>The postoperative daily changes from the baseline (preoperative) Fourier coefficients of walking acceleration signals until 3 months after total knee arthroplasty</measure>
      <time_frame>Two weeks before the surgery until three months after the surgery</time_frame>
      <description>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.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>The postoperative changes from the baseline (preoperative) Fourier coefficients of walking acceleration signals at 12 months after total knee arthroplasty</measure>
      <time_frame>Two weeks before and 12 months after the surgery</time_frame>
      <description>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.</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>The postoperative daily changes from the baseline (preoperative) cadence until 3 months after total knee arthroplasty</measure>
      <time_frame>Two weeks before the surgery until three months after the surgery</time_frame>
      <description>Acceleration data will be analyzed to determine the gait cadence defined as the number of steps taken per minute.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>The postoperative daily changes from the baseline (preoperative) stance time until 3 months after total knee arthroplasty</measure>
      <time_frame>Two weeks before the surgery until three months after the surgery</time_frame>
      <description>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.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>The postoperative daily changes from the baseline (preoperative) rest time until 3 months after total knee arthroplasty</measure>
      <time_frame>Two weeks before the surgery until three months after the surgery</time_frame>
      <description>Acceleration data will be analyzed to determine the proportion of time the patient spent lying down during each day expressed as the percentage.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>The postoperative daily changes from the baseline (preoperative) sitting time until 3 months after total knee arthroplasty</measure>
      <time_frame>Two weeks before the surgery until three months after the surgery</time_frame>
      <description>Acceleration data will be analyzed to determine the proportion of time the patient spent sitting during each day expressed as the percentage.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>The postoperative daily changes from the baseline (preoperative) standing time until 3 months after total knee arthroplasty</measure>
      <time_frame>Two weeks before the surgery until three months after the surgery</time_frame>
      <description>Acceleration data will be analyzed to determine the proportion of time the patient spent standing during each day expressed as the percentage.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>The postoperative daily changes from the baseline (preoperative) walking time until 3 months after total knee arthroplasty</measure>
      <time_frame>Two weeks before the surgery until three months after the surgery</time_frame>
      <description>Acceleration data will be analyzed to determine the proportion of time the patient spent walking during each day expressed as the percentage.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>The postoperative daily changes from the baseline (preoperative) step counts until 3 months after total knee arthroplasty</measure>
      <time_frame>Two weeks before the surgery until three months after the surgery</time_frame>
      <description>Acceleration data will be analyzed to determine the number of steps the patient takes during each day.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>The postoperative daily changes from the baseline (preoperative) sit-to-stand counts until 3 months after total knee arthroplasty</measure>
      <time_frame>Two weeks before the surgery until three months after the surgery</time_frame>
      <description>Acceleration data will be analyzed to determine the number of sit-to-stands the patient performs during each day.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>The postoperative daily changes from the baseline (preoperative) activity count index until 3 months after total knee arthroplasty</measure>
      <time_frame>Two weeks before the surgery until three months after the surgery</time_frame>
      <description>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.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>The postoperative changes from the baseline (preoperative) cadence at 12 months after total knee arthroplasty</measure>
      <time_frame>Two weeks before and 12 months after the surgery</time_frame>
      <description>Acceleration data will be analyzed to determine the gait cadence defined as the number of steps taken per minute.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>The postoperative changes from the baseline (preoperative) stance time at 12 months after total knee arthroplasty</measure>
      <time_frame>Two weeks before and 12 months after the surgery</time_frame>
      <description>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.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>The postoperative changes from the baseline (preoperative) rest time at 12 months after total knee arthroplasty</measure>
      <time_frame>Two weeks before and 12 months after the surgery</time_frame>
      <description>Acceleration data will be analyzed to determine the proportion of time the patient spent lying down during each day expressed as the percentage.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>The postoperative changes from the baseline (preoperative) sitting time at 12 months after total knee arthroplasty</measure>
      <time_frame>Two weeks before and 12 months after the surgery</time_frame>
      <description>Acceleration data will be analyzed to determine the proportion of time the patient spent sitting during each day expressed as the percentage.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>The postoperative changes from the baseline (preoperative) standing time at 12 months after total knee arthroplasty</measure>
      <time_frame>Two weeks before and 12 months after the surgery</time_frame>
      <description>Acceleration data will be analyzed to determine the proportion of time the patient spent standing during each day expressed as the percentage.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>The postoperative changes from the baseline (preoperative) walking time at 12 months after total knee arthroplasty</measure>
      <time_frame>Two weeks before and 12 months after the surgery</time_frame>
      <description>Acceleration data will be analyzed to determine the proportion of time the patient spent walking during each day expressed as the percentage.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>The postoperative changes from the baseline (preoperative) step counts at 12 months after total knee arthroplasty</measure>
      <time_frame>Two weeks before and 12 months after the surgery</time_frame>
      <description>Acceleration data will be analyzed to determine the number of steps the patient takes during each day.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>The postoperative changes from the baseline (preoperative) sit-to-stand counts at 12 months after total knee arthroplasty</measure>
      <time_frame>Two weeks before and 12 months after the surgery</time_frame>
      <description>Acceleration data will be analyzed to determine the number of sit-to-stands the patient performs during each day.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>The postoperative changes from the baseline (preoperative) activity count index at 12 months after total knee arthroplasty</measure>
      <time_frame>Two weeks before and 12 months after the surgery</time_frame>
      <description>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.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>The change in Knee injury and Osteoarthritis Outcome Score (KOOS)</measure>
      <time_frame>Baseline (2 weeks before surgery); and 1 month, 2 months, 3 months, and 12 months after the surgery.</time_frame>
      <description>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.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>The change in EuroQol-5 Dimensions-3 Levels (EQ-5D-3L) questionnaire</measure>
      <time_frame>Baseline (2 weeks before surgery); and 1 month, 2 months, 3 months, and 12 months after the surgery.</time_frame>
      <description>EQ-5D-3L is a generic tool for Patient Reported Outcomes (PRO) measurement that can assess patients' quality of life, irrespective of the disease.</description>
    </secondary_outcome>
    <enrollment type="Anticipated">30</enrollment>
    <condition>Arthroplasty, Replacement, Knee</condition>
    <intervention>
      <intervention_type>Other</intervention_type>
      <intervention_name>Measuring gait</intervention_name>
      <description>Measurements are performed by PA trackers. The measurements include:&#xD;
  Lower limb 3D linear accelerations according to gait bouts collected and stored as time-series of linear accelerations with a frequency of 12.5 Hz.&#xD;
  Details on the amount of time spent lying down, sitting, and standing, as well as the number of steps and sit-to-stands completed throughout the day. This information will be stored in 15-minute epochs and then summarized into daily summaries for analysis.</description>
      <other_name>Measuring PA levels</other_name>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          All eligible patients who are scheduled for TKA from a certain date onwards will be&#xD;
          screened and offered participation in the study.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Patients diagnosed with knee OA and scheduled to undergo unilateral TKA at Aalborg&#xD;
               University Hospital, Farsø.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Daily pain (numeric rating scale (NRS) ≥ 4) or severe OA in the contralateral knee&#xD;
               (KL-IV)&#xD;
  &#xD;
            -  Daily pain (NRS ≥ 4) or severe OA in the spine and other lower limb joints&#xD;
  &#xD;
            -  BMI &gt; 35 kg/m2&#xD;
  &#xD;
            -  Recent surgery in the spine or lower limbs (&lt; 6 months)&#xD;
  &#xD;
            -  Neurological movement disorders&#xD;
  &#xD;
            -  Inflammatory arthritis&#xD;
  &#xD;
            -  Patients who are not smartphone users&#xD;
  &#xD;
            -  Frail patients with clinical frailty scale (CFS)10 ≥ 5&#xD;
  &#xD;
            -  Residents of nursing homes&#xD;
  &#xD;
            -  Patients dependent to walking aids for ambulation&#xD;
  &#xD;
            -  Patients with dementia or memory problems&#xD;
  &#xD;
            -  Patients with skin sensitivity or issues at the location of the plasters.&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <gender_based>Yes</gender_based>
      <minimum_age>18 Years</minimum_age>
      <maximum_age>N/A</maximum_age>
      <healthy_volunteers>No</healthy_volunteers>
    </eligibility>
    <overall_official>
      <last_name>Ole Rahbek, MD, PhD</last_name>
      <role>Principal Investigator</role>
      <affiliation>Aalborg University Hospital</affiliation>
    </overall_official>
    <overall_contact>
      <last_name>Arash Ghaffari, MD</last_name>
      <phone>+45-91483966</phone>
      <email>a.ghaffari@rn.dk</email>
    </overall_contact>
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    </reference>
    <verification_date>August 2023</verification_date>
    <study_first_submitted>July 13, 2023</study_first_submitted>
    <study_first_submitted_qc>August 7, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 15, 2023</study_first_posted>
    <last_update_submitted>August 23, 2023</last_update_submitted>
    <last_update_submitted_qc>August 23, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 25, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>Aalborg University Hospital</investigator_affiliation>
      <investigator_full_name>Ole Rahbek</investigator_full_name>
      <investigator_title>Professor</investigator_title>
    </responsible_party>
    <patient_data>
      <sharing_ipd>Undecided</sharing_ipd>
    </patient_data>
    <!-- Results have not yet been posted for this study                                          -->
  </clinical_study>
  
  
text: download date: ClinicalTrials.gov processed this data on September 20, 2023
  link text: Link to the current ClinicalTrials.gov record.
  url: https://clinicaltrials.gov/ct2/show/NCT05992064
  org study id: F2022-196
  nct id: NCT05992064
  lead sponsor: 
  collaborator: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  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)
  
          -  At least two weeks before the surgery, patients will be enrolled in the study and given
             detailed information about the protocol. Informed consent will be obtained, and basic
             patient information, including their civil registration number, age, sex, comorbidities,
             surgical history, height, weight, and lower limb lengths, will be collected. In
             addition, patients will be given knee injury and osteoarthritis outcome score (KOOS) and
             European Quality of Life 5 Dimensions 3 Level Version (EQ-5D-3L) questionnaires to
             complete. The investigators will also evaluate the patients' frailty level using CFS.
             All data will be recorded in a REDCap database hosted by region of North Jutland,
             Denmark.
  
          -  The location of the sensors on the distal thigh of the affected knee will be marked, and
             a PA tracker (SENS Motion) will be attached to the specified location. Patients will
             then be asked to walk at a self-selected speed for about two minutes to register a
             template for their walking pattern. The SENS app will be installed on the patients'
             smartphones. Next, the patients will be instructed on transferring data from sensors'
             internal memory to the SENS cloud system, changing the adhesives, and attaching the
             sensor to the exact location specified by the markers. Patients will be provided enough
             plasters and a marker pen, and instructional material will guide them through the
             process. Participants will be instructed to wear the devices at all times, even when
             showering or swimming.
  
        B. Pre-op period
  
          -  The PA tracker will continuously record the patients' PA levels and lower limb
             accelerations. The collected data will be transferred daily from the PA tracker to the
             SENS cloud system.
  
          -  To ensure the accuracy and completeness of the data, the investigators will monitor the
             data transfer process. Specifically, the data collected by the PA tracker will be
             transferred from the SENS cloud system to the Q-drive every week. If a participant fails
             to transfer their data within 72 hours, they will be contacted to remind them to do so.
  
          -  All data collected during pre-op will be used to establish the participants' baseline PA
             levels. This information will be used as a reference point to assess changes in PA
             levels and lower limb accelerations after surgery and during the follow-up 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
  
          -  After surgery, participants will be instructed to wear the tracker continuously for
             three months. During the postoperative period, the investigators will closely monitor
             patients' PA levels. Patients who fail to transfer sensor data within 72 hours will be
             contacted to ensure continuous data collection. The sensor data will be transferred
             weekly from the SENS cloud system to Q-drive. The investigators will also administer
             weekly EQ-5D-3L questionnaires and monthly KOOS questionnaires to assess patients'
             health status and knee function through the REDCap platform.
  
          -  After three months, the sensors will be turned off and returned to the hospital. The
             investigators will extract relevant information from patients' records to identify
             possible complications and classify them based on the standardized list and definitions
             of the Knee Society 14.
  
        E. 12-months follow up
  
          -  The follow-up period will consist of an additional two weeks of data collection one year
             after surgery, during which participants will continue to wear the tracker to assess the
             changes in PA levels and lower limb accelerations.
  
          -  At the 12-month follow-up, patients will be invited to participate in the follow-up part
             of the study, and if they consent, they will receive sensors to wear continuously for 14
             days. Data will be collected continuously during this period, and KOOS and EQ-5D-3L
             questionnaires will be administered through REDCap. In addition, the patients' journals
             will be reviewed for any potential complications.
  
          -  Once the 14-day period is complete, the sensors will be deactivated and returned to the
             hospital.
  observational model: Cohort
  time perspective: Prospective
  measure: The postoperative daily changes from the baseline (preoperative) Fourier coefficients of walking acceleration signals until 3 months after total knee arthroplasty
  time frame: Two weeks before the surgery until three months after the surgery
  description: 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.
  measure: The postoperative changes from the baseline (preoperative) Fourier coefficients of walking acceleration signals at 12 months after total knee arthroplasty
  time frame: Two weeks before and 12 months after the surgery
  description: 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.
  measure: The postoperative daily changes from the baseline (preoperative) cadence until 3 months after total knee arthroplasty
  time frame: Two weeks before the surgery until three months after the surgery
  description: Acceleration data will be analyzed to determine the gait cadence defined as the number of steps taken per minute.
  measure: The postoperative daily changes from the baseline (preoperative) stance time until 3 months after total knee arthroplasty
  time frame: Two weeks before the surgery until three months after the surgery
  description: 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.
  measure: The postoperative daily changes from the baseline (preoperative) rest time until 3 months after total knee arthroplasty
  time frame: Two weeks before the surgery until three months after the surgery
  description: Acceleration data will be analyzed to determine the proportion of time the patient spent lying down during each day expressed as the percentage.
  measure: The postoperative daily changes from the baseline (preoperative) sitting time until 3 months after total knee arthroplasty
  time frame: Two weeks before the surgery until three months after the surgery
  description: Acceleration data will be analyzed to determine the proportion of time the patient spent sitting during each day expressed as the percentage.
  measure: The postoperative daily changes from the baseline (preoperative) standing time until 3 months after total knee arthroplasty
  time frame: Two weeks before the surgery until three months after the surgery
  description: Acceleration data will be analyzed to determine the proportion of time the patient spent standing during each day expressed as the percentage.
  measure: The postoperative daily changes from the baseline (preoperative) walking time until 3 months after total knee arthroplasty
  time frame: Two weeks before the surgery until three months after the surgery
  description: Acceleration data will be analyzed to determine the proportion of time the patient spent walking during each day expressed as the percentage.
  measure: The postoperative daily changes from the baseline (preoperative) step counts until 3 months after total knee arthroplasty
  time frame: Two weeks before the surgery until three months after the surgery
  description: Acceleration data will be analyzed to determine the number of steps the patient takes during each day.
  measure: The postoperative daily changes from the baseline (preoperative) sit-to-stand counts until 3 months after total knee arthroplasty
  time frame: Two weeks before the surgery until three months after the surgery
  description: Acceleration data will be analyzed to determine the number of sit-to-stands the patient performs during each day.
  measure: The postoperative daily changes from the baseline (preoperative) activity count index until 3 months after total knee arthroplasty
  time frame: Two weeks before the surgery until three months after the surgery
  description: 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.
  measure: The postoperative changes from the baseline (preoperative) cadence at 12 months after total knee arthroplasty
  time frame: Two weeks before and 12 months after the surgery
  description: Acceleration data will be analyzed to determine the gait cadence defined as the number of steps taken per minute.
  measure: The postoperative changes from the baseline (preoperative) stance time at 12 months after total knee arthroplasty
  time frame: Two weeks before and 12 months after the surgery
  description: 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.
  measure: The postoperative changes from the baseline (preoperative) rest time at 12 months after total knee arthroplasty
  time frame: Two weeks before and 12 months after the surgery
  description: Acceleration data will be analyzed to determine the proportion of time the patient spent lying down during each day expressed as the percentage.
  measure: The postoperative changes from the baseline (preoperative) sitting time at 12 months after total knee arthroplasty
  time frame: Two weeks before and 12 months after the surgery
  description: Acceleration data will be analyzed to determine the proportion of time the patient spent sitting during each day expressed as the percentage.
  measure: The postoperative changes from the baseline (preoperative) standing time at 12 months after total knee arthroplasty
  time frame: Two weeks before and 12 months after the surgery
  description: Acceleration data will be analyzed to determine the proportion of time the patient spent standing during each day expressed as the percentage.
  measure: The postoperative changes from the baseline (preoperative) walking time at 12 months after total knee arthroplasty
  time frame: Two weeks before and 12 months after the surgery
  description: Acceleration data will be analyzed to determine the proportion of time the patient spent walking during each day expressed as the percentage.
  measure: The postoperative changes from the baseline (preoperative) step counts at 12 months after total knee arthroplasty
  time frame: Two weeks before and 12 months after the surgery
  description: Acceleration data will be analyzed to determine the number of steps the patient takes during each day.
  measure: The postoperative changes from the baseline (preoperative) sit-to-stand counts at 12 months after total knee arthroplasty
  time frame: Two weeks before and 12 months after the surgery
  description: Acceleration data will be analyzed to determine the number of sit-to-stands the patient performs during each day.
  measure: The postoperative changes from the baseline (preoperative) activity count index at 12 months after total knee arthroplasty
  time frame: Two weeks before and 12 months after the surgery
  description: 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.
  measure: The change in Knee injury and Osteoarthritis Outcome Score (KOOS)
  time frame: Baseline (2 weeks before surgery); and 1 month, 2 months, 3 months, and 12 months after the surgery.
  description: 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.
  measure: The change in EuroQol-5 Dimensions-3 Levels (EQ-5D-3L) questionnaire
  time frame: Baseline (2 weeks before surgery); and 1 month, 2 months, 3 months, and 12 months after the surgery.
  description: EQ-5D-3L is a generic tool for Patient Reported Outcomes (PRO) measurement that can assess patients' quality of life, irrespective of the disease.
  intervention type: Other
  intervention name: Measuring gait
  description: Measurements are performed by PA trackers. The measurements include:
  Lower limb 3D linear accelerations according to gait bouts collected and stored as time-series of linear accelerations with a frequency of 12.5 Hz.
  Details on the amount of time spent lying down, sitting, and standing, as well as the number of steps and sit-to-stands completed throughout the day. This information will be stored in 15-minute epochs and then summarized into daily summaries for analysis.
  other name: Measuring PA levels
  study pop: 
  sampling method: Probability Sample
  criteria: 
  gender: All
  gender based: Yes
  minimum age: 18 Years
  maximum age: N/A
  healthy volunteers: No
  last name: Ole Rahbek, MD, PhD
  role: Principal Investigator
  affiliation: Aalborg University Hospital
  last name: Arash Ghaffari, MD
  phone: +45-91483966
  email: a.ghaffari@rn.dk
  citation: 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.
  PMID: 28690473
  citation: 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.
  PMID: 29416347
  citation: 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.
  PMID: 34372377
  citation: 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.
  PMID: 30911852
  citation: 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.
  PMID: 16534088
  citation: 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.
  PMID: 27417610
  citation: 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.
  PMID: 1560799
  citation: 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.
  PMID: 36633891
  citation: 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.
  PMID: 32010456
  citation: Mendiratta P, Schoo C, Latif R. Clinical Frailty Scale. 2023 Apr 23. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2023 Jan-. Available from http://www.ncbi.nlm.nih.gov/books/NBK559009/
  PMID: 32644435
  citation: 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.
  PMID: 35890969
  citation: 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.
  PMID: 9699158
  citation: 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.
  PMID: 11491192
  citation: 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.
  PMID: 22810157
  responsible party type: Principal Investigator
  investigator affiliation: Aalborg University Hospital
  investigator full name: Ole Rahbek
  investigator title: Professor
  sharing ipd: Undecided