Hospitalized Patients, Physical Inactivity
Conditions
Keywords
Movement detection, Activity sensors, Algorithm development and validation, Hospital implementation
Brief summary
The goal of this monocentric observational study involving hospitalised patients is to assess the integration of activity sensors into routine clinical practice.
Detailed description
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.
Interventions
None listed
Sponsors
Zurich University of Applied Sciences
University Hospital, Basel, Switzerland
Study design
Observational model
CASE_ONLY
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* at least 3 days inpatient on the medical ward 6.1 (Monday to a maximum of Thursday or Tuesday to a maximum of Friday) * at least restricted bed rest * patient must be able to walk * patient must be cognitively able to follow instructions (normal Observation Screening Scale (DOS) und modified Confusion Assessment Method (mCAM)) * ≥ 18 years * signed informed consent
Exclusion criteria
* planned discharge within the next 3 days * planned surgery during the measurement * isolated patient * inability or contraindications to participate in the study or to follow the study procedures, e.g. due to certain neurological disorders, speech problems, mental disorders, or cognitive impairments * prior inclusion in the study
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Evaluation of the time expenditure | Day 1-4 | Using a study-specific questionnaire, the additional time expenditure associated with the use of activity sensors by physiotherapy and clinical care is evaluated. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Assessment of the comfort level associated with wearing the sensors | Day 2-4 | 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. |
| Evaluation of the handling | After 10-week period (at recruitment completion) | 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. |
| Assessment of the accuracy of the classification algorithm for the detection of movements parameters | Day 1-3 | 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. |
| Optimization of activity classification algorithm | After 10-week period (at recruitment completion) | If the algorithm incorrectly classifies activities, a detailed analysis will be conducted after the recruitment phase to optimize the system accordingly. |
Countries
Switzerland
Outcome results
None listed