The goal of this monocentric observational study involving acute hospitalised patients is to validate the accuracy of classification algorithms for the detection of various movements parameters.

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. A pilot study (NCT06403826) involving 40 patients examined the feasibility and effectiveness of using activity sensors in clinical settings. The study focused on detecting specific activities and movement patterns using sensors worn on various body parts, with the ankle identified as the preferred location for long-term monitoring. Most participants found wearing the sensors tolerable, indicating the practicality of this approach for extended patient activity monitoring.

The primary objective of this observational, single center study is to validate the accuracy of newly developed algorithms for the detection of various movements parameters. Two different sensors worn on the ankle are used to record the duration of lying, sitting/standing, and the number of steps taken when walking and climbing stairs (up/down). The validation is based on the movement data collected from this and the pilot study and will be conducted in two phases:

• phase 1 (model improvement): Increasing the robustness of the algorithms using training data
• phase 2 (model validation): Validation of the algorithms using test data

The secondary objectives are:

• to determine if altitude data enhances the algorithms' accuracy in distinguishing between stair climbing and walking
• to evaluate the comfort of wearing the sensors

The results of this study 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.

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)

Acute hospitalized patients are continuously recruited by the project management team within the context of daily clinical practice from both medical and surgical wards at the University Hospital Basel. Recruitment takes place as soon as possible after admission to the hospital.