Hospitalized Patients, Physical Inactivity
Conditions
Keywords
Movement detection, Activity sensors, Sensor positioning, Algorithm development
Brief summary
The goal of this monocentric observational study involving acute hospitalised patients is to develop a classification algorithm for the detection of various movements parameters.
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. In order to quantify the amount of physical activity of hospitalised patients, the ability of activity sensors to distinguish between lying, sitting, standing and walking is an important requirement. The primary objective of this observational, single center study is to develop an algorithm for the detection of various movements parameters. Therefore, enrolled patients, wearing a sensor on either ankle, wrist, or upper thigh, perform a fixed set of movements to generate acceleration and movement data. The secondary objective is the evaluation of the best suited position of wearing the sensor. The results of this pilot study will provide information about the feasibility and effectiveness of using activity sensors in clinical settings and will advance healthcare by developing an algorithm that accurately determines the activity patterns of hospitalized patients, thereby enhancing monitoring and understanding of patient mobility in hospital settings.
Interventions
None listed
Sponsors
Zurich University of Applied Sciences
University Hospital, Basel, Switzerland
Study design
Observational model
CASE_ONLY
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* patient must have been able to walk before hospitalisation (with or without aids) * patient must be cognitively able to follow instructions (if a cognitive assessment has been carried out, this cut-off value counts, if no assessment is available, no cognitive impairment is assumed) * ≥ 18 years * signed informed consent
Exclusion criteria
* patient unable to move prior to hospital admission * prior inclusion in the study * discharge on the same day * inability or contraindications to participate in the study or to follow the study procedures, e.g. due to certain neurological disorders (such as Parkinsonism, hemiplegia, severe Multiple Sclerosis), speech problems, mental disorders, or cognitive impairments isolated patient (unable to complete the test battery completely)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Development of a classification algorithm for the detection of movements parameters | 5 days during hospitalization (up to 1 hour per day) | Patients undergo a test battery that contains a fixed sequence of movements. A sensor on either the ankle, wrist or outer thigh is used to measure patient movement accurately and continuously and to generate acceleration and gryoscope data. These data, including duration of lying, sitting and standing, number of sit-to-stand repetitions, steps taken when walking and climbing stairs (up/down), and distance covered while walking, are used for the development of a classification algorithm for the detection of movements parameters. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Evaluation of best sensor positioning | 5 days during hospitalization (up to 6 hours per day) | To determine the best position of the sensor, the different positions (ankle, wrist or outer thigh) are evaluated in terms of the accuracy of the developed classification algorithm, the completeness of the continuously collected data, and the comfort level associated with wearing the sensor over the entire data collection period. For the latter, a questionnaire is used. |
Countries
Switzerland
Outcome results
None listed