Total knee arthroplasty (TKA) is one of the most frequent orthopedic procedures. Over 50% of patients report higher expectations than their surgeons, and 10-50% remain dissatisfied postoperatively. Persistent pain, functional limitations, and unmet expectations are key drivers. Identifying risks pre- and early postoperatively is essential, alongside empowering patients through self-management. Existing scoring systems integrate PROMs, demographics, and sometimes imaging but within limited timeframes. They rarely capture functional deficits or long-term trajectories. Digital health solutions for TKA (pre-)rehabilitation exist, yet most focus on physiotherapy and education rather than predictive outcome modeling. To address this gap, the study team has developed a novel mobile application that enables the documentation and analysis of movement data up to 10 years before surgery and throughout long-term follow-up. These data are combined with PROMs and functional test results, providing a unique basis for outcome prediction and risk stratification in TKA. Primary Objective The aim of this pilot study is to develop a composite outcome score for TKA patients. This score will integrate demographic variables, PROMs, and objective functional measures (knee joint angles, gait parameters, walk tests) to identify risk factors for dissatisfaction and support predictive modeling. A machine learning algorithm will be trained using the collected dataset to predict patient satisfaction after TKA. Endpoints Primary endpoint: Overall patient satisfaction Secondary endpoints: Age, height, weight, step count, step length, gait asymmetry, gait speed, double support phase, knee joint ROM, walk test, KOOS, SF-36, satisfaction with the app, and satisfaction with app use. Study Population Inclusion criteria are patients ≥18 years before or after TKA. Exclusion criteria include missing consent, ineligible diagnosis, lack of iOS smartphone, age \<18 years, or insufficient German language skills (as no English version of the app is currently available). Planned enrollment: 450 patients. Methods Design: Single-center, prospective pilot study. The app collects patient-authorized movement data already stored on smartphones as well as future data. Participants choose which data to share. In addition, they are prompted to complete gait tests and knee function tests. PROMs (KOOS, SF-36, satisfaction) are administered at regular intervals.
Background: In routine medical practice, only limited objective long-term data on patients' physical activity and its progression during the course of disease are typically available. Similarly, longitudinal information from patient-reported outcome measures (PROMs) or functional tests is often lacking. In musculoskeletal disorders in particular, combining subjective complaints with data on patients' everyday movement behavior is crucial for guiding further diagnostic or therapeutic decisions and for risk assessment. Following total knee arthroplasty (TKA), more than 50% of patients report higher expectations for their surgical outcome than their treating surgeons. Between 10% and 50% of patients remain dissatisfied with the overall outcome after TKA. Persistent knee pain is among the main reasons for such dissatisfaction. Functional musculoskeletal limitations and unmet expectations regarding the outcome further contribute to long-term dissatisfaction. Additional risk factors for an unsatisfactory outcome include younger age, female sex, higher BMI, limited radiographic severity of osteoarthritis preoperatively (likely reflecting minor functional deficits), and psychological factors such as catastrophizing, depression, and anxiety. To sustainably improve patient satisfaction, these risk factors must be identified and analyzed both preoperatively and in the early postoperative period, and the results must be communicated clearly to patients and healthcare providers. Patient self-management plays an essential role in this context. Patients should be empowered to actively improve their postoperative outcomes by addressing modifiable risk factors through targeted self-directed strategies. Existing scoring systems generally focus on integrating clinical parameters such as PROMs, demographic data, and, in some cases, imaging within limited timeframes. However, they usually do not incorporate musculoskeletal functional deficits or long-term continuous pre- and postoperative trajectories. With the ongoing digitalization of medicine, various systems for prehabilitation and rehabilitation in the context of TKA are already available. Current systems for digital prehabilitation and rehabilitation generally focus on physiotherapeutic self-exercises and patient education regarding the pre- and postoperative disease course, but they do not address outcome prediction. Primary Objective of the Pilot Study The primary objective of this pilot study is the development of an outcome score for patients before and after implantation of a knee joint endoprosthesis using a self-developed app. This score will combine demographic and subjective parameters (PROMs) with objective functional parameters (knee joint angle measurement, gait parameters, and the 6-minute walk test) to provide the most comprehensive picture possible of risk factors and deficits associated with patient dissatisfaction. Based on the collected data, a machine learning algorithm will be developed to predict outcomes with respect to patient satisfaction. Null hypothesis (H₀): A combined score consisting of demographic data, PROMs (Patient-Reported Outcome Measures), and objective functional parameters (knee joint angle, gait parameters, walk test) does not allow significant prediction of postoperative patient satisfaction after knee arthroplasty at different time points. Alternative hypothesis (H₁): A combined score consisting of demographic data, PROMs, and objective functional parameters does allow significant prediction of postoperative patient satisfaction after knee arthroplasty at different time points. Endpoints / Outcome Measures Primary endpoint: Overall satisfaction with clinical course Secondary endpoints: Age, height, weight, step count, step length, gait asymmetry, gait speed, double support phase, knee joint ROM, walk test, satisfaction with app, KOOS, SF-36 Study Population Inclusion criteria: All patients over 18 years of age before and after implantation of a knee joint endoprosthesis, or with gonarthrosis alone. Exclusion criteria: Missing informed consent; diagnosis not meeting the inclusion criteria; no smartphone with iOS operating system; non-German speaking; age \<18 years. Handling of non-German-speaking individuals: Since no English version of the app is currently available, non-German-speaking individuals cannot participate in the study. Planned number of participants: 450 patients Recruitment measures: Recruitment will be conducted via flyers and posters displayed in the routine arthroplasty outpatient clinic of the Department of Orthopaedics and Sports Orthopaedics. Patients can download the app using a QR code provided in the information materials. Access to the app is granted during the outpatient visit at the Department of Orthopaedics and Sports Orthopaedics once the patient consents to participate in the study. Written informed consent is obtained by the attending physicians of the Department of Orthopaedics and Sports Orthopaedics at TUM University Hospital, who will have received detailed training from the study leadership. Participants may contact the study team at any time with questions. Additionally, a help tool will be integrated into the app for patient inquiries and feedback. No financial compensation will be provided to participants. No insurance has been taken out for participants. Methodology and Implementation Study design: Single-center, prospective pilot study Study procedure: The app collects patient-authorized movement data and analyzes these data. Participants may select which data they wish to share. In addition, participants are prompted at regular intervals to perform and document functional tests, such as gait tests and knee function tests. Standardized PROMs (KOOS, SF-36, satisfaction) are collected at regular intervals. Data collection begins immediately after app installation and ends at the latest two years after initiation of data collection. Description of Data Sources The data used in this study are derived from two main sources: 1. movement data provided by the participants' mobile devices (e.g., step count, step length, gait speed, knee joint angle, etc.), which are integrated into the app following the users' explicit consent; and 2. subjective health data collected at regular intervals via standardized questionnaires integrated into the app (e.g., KOOS, SF-36, satisfaction). The app accesses only those movement data supplied by the iOS operating system via HealthKit and explicitly released by the user. Data processing occurs solely with the participants' active consent for each respective data category. The selection and processing of these data sources have been coordinated with the Data Protection Officer of TUM University Hospital and have undergone a formal data protection assessment. All applicable data protection requirements are fully observed, particularly with respect to pseudonymization, data security, and purpose limitation. Data Management and Data Protection In this study, the University Hospital of the Technical University of Munich (TUM Klinikum), Ismaninger Straße 22, 81675 Munich, is responsible for data processing. The legal basis for processing is the personal consent of participants in accordance with Art. 6(1)(a) and Art. 9(2)(a) GDPR. All data will be treated confidentially at all times. Access to the collected data is restricted to authorized members of the study team. Data will only be stored as long as necessary for the conduct and scientific evaluation of the study, but for no longer than 10 years after study completion. Participation in the study and consent to data processing are voluntary. Participants may withdraw their consent at any time without providing reasons and without any disadvantage to themselves.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
SCREENING
Masking
NONE
Enrollment
450
Our app collects patient-authorized mobility data that are already stored on the patient's smartphone and those that will be recorded in the future, and analyzes these data with respect to their temporal patterns. Each study participant can decide individually which data they wish to share. In addition, participants are prompted at regular intervals to perform and document functional tests, such as gait assessments (once per month) and knee function tests (weekly). Furthermore, subjective health data are regularly collected using standardized PROMs: KOOS and SF-36 every three weeks, patient-reported satisfaction with health status on a monthly basis, and user satisfaction with the app every three months. The collected data can be accessed both by the patient and the study team and can be downloaded and stored as a PDF file if needed. Participation in the study requires a single on-site visit at the Department and Outpatient Clinic of Orthopedics and Sports Orthopedics. No further in-pe
Department of Orthopaedics and Sports Orthopaedics TUM University Hospital Rechts der Isar
Munich, Bavaria, Germany
overall patient satisfaction
satisfaction scale minimum: 0 = not satisfied, maximum: 5 = fully satisfied
Time frame: through study completion, an average of 1 year
age
age in years
Time frame: through study completion, an average of 1 year
sex
sex in male, female, diverse
Time frame: through study completion, an average of 1 year
height
height in centimeters
Time frame: through study completion, an average of 1 year
Weight
weight in kilograms, weight and height will be combined to report BMI in kg/m\^2
Time frame: through study completion, an average of 1 year
KOOS
The KOOS (Knee injury and Osteoarthritis Outcome Score) is a 42-item, patient-reported questionnaire assessing knee-related symptoms, pain, activities of daily living, sport/recreation, and quality of life, with scores ranging from 0 (extreme problems) to 100 (no problems).
Time frame: through study completion, an average of 1 year
SF-36
The SF-36 is a 36-item, patient-reported questionnaire assessing health-related quality of life across eight domains, with scores ranging from 0 (worst health) to 100 (best health).
Time frame: through study completion, an average of 1 year
Knee range of motion
The test measures knee joint mobility in degrees from -10° to 130°.
Time frame: through study completion, an average of 1 year
2-Minute walking test
The test measures the distance walked in meters in 2 minutes.
Time frame: through study completion, an average of 1 year
App feedback
The questionnaire contains three questions about satisfaction with the app and allows for free comments on wishes and suggestions.
Time frame: through study completion, an average of 1 year
step count
Measures the number of steps in absolute numbers per day
Time frame: through study completion, an average of 1 year
stride length
Measures stride length in meters
Time frame: through study completion, an average of 1 year
gait speed
Measures walking speed in meters per second
Time frame: through study completion, an average of 1 year
double stance phase
The double stance phase is measured by the iPhone's inertial sensors using gait cycle algorithms and expressed as a percentage of total gait cycle time (%).
Time frame: through study completion, an average of 1 year
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