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| Name | Class |
|---|---|
| Zurich University of Applied Sciences | OTHER |
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The goal of this monocentric observational study involving hospitalised patients is to assess the integration of activity sensors into routine clinical practice.
Patients in hospitals spend the majority of their time inactive, sitting or lying down. Not being active is a common problem for patients in hospitals, often causing complications and impairing recovery, as it can lead to issues such as reduced blood volume, unsteady blood pressure when standing, weaker muscles, and a higher risk of infections, blood clots, and other health issues. The inactivity-related changes in the body in combination with the natural ageing process, the stress of being in the hospital, a poor nutritional status, and possibly troubles with thinking, memory, and understanding or depression diminish the ability to regenerate with overall compromised physiological resilience.
A pilot study (NCT06403826) involving 40 patients demonstrated the feasibility and effectiveness of using activity sensors in clinical settings. A subsequent validation study (NCT06396676) validated a classification model based on activity data from 65 patients, which can distinguish between different activities with 89% accuracy.
The integration of activity sensors into routine clinical practice requires a comprehensive infrastructure to support interdisciplinary collaboration. Therefore, the primary objective of this observational, single center study is to evaluate the additional time expenditure associated with using activity sensors in routine clinical practice by physiotherapy and clinical care over a 10-week period. Secondary objectives include assessing the comfort of extended sensor use, the feasibility and benefits for healthcare professionals, the reliability and accuracy of the sensor data, and the optimization of the activity classification algorithm.
The results of this study will contribute to improving patient care through the use of activity sensors, enabling more personalized care.
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| Measure | Description | Time Frame |
|---|---|---|
| Evaluation of the time expenditure | Using a study-specific questionnaire, the additional time expenditure associated with the use of activity sensors by physiotherapy and clinical care is evaluated. | Day 1-4 |
| Measure | Description | Time Frame |
|---|---|---|
| Assessment of the comfort level associated with wearing the sensors | The comfort of wearing the sensors is evaluated by a questionnaire. The responses from patients are being collected regarding the discomfort of wearing the sensors or any problems with the attachment. | Day 2-4 |
| Evaluation of the handling |
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Inclusion Criteria:
Exclusion Criteria:
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Patients that are hospitalised for at least 3 days on the medical ward 6.1 of the University Hospital Basel and are recruited either mondays or tuesdays.
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| Name | Affiliation | Role |
|---|---|---|
| Joris Kirchberger | University Hospital, Basel, Switzerland | Principal Investigator |
| Jens Eckstein, Prof. Dr. med. | University Hospital, Basel, Switzerland | Study Chair |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Universitiy Hospital Basel, Division of Internal Medicine | Basel | Canton of Basel-City | 4031 | Switzerland |
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| ID | Term |
|---|---|
| D057185 | Sedentary Behavior |
| ID | Term |
|---|---|
| D001519 | Behavior |
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Evaluation of the handling of activity sensors and the possibilities for integrating the sensors into daily hospital practice through an open, study-specific interview after recruitment completion. |
| After 10-week period (at recruitment completion) |
| Assessment of the accuracy of the classification algorithm for the detection of movements parameters | The activity sensor is used to continuously collect data. Additionally, once a day a spot measurement is taken. It is checked whether the algorithm of the activity sensors aligns with the manual recording of various movements. The accuracy of the algorithm is calculated using a multi-class confusion matrix. The rows are the actual classes and the columns are the predicted classes. The diagonal of the matrix contains the observations where the predicted class matches the actual class (true positive). Accuracy [in %]= Sum of the diagonal elements / Total number of observations * 100. This will ensure the reliability and accuracy of the recorded data, as well as allow for the verification of data loss over multiple days. | Day 1-3 |
| Optimization of activity classification algorithm | If the algorithm incorrectly classifies activities, a detailed analysis will be conducted after the recruitment phase to optimize the system accordingly. | After 10-week period (at recruitment completion) |