SHIFT-Hospital in Motion (Hospital Implementation Study)

SHIFT-Hospital in Motion: Use of Activity Sensors in the Clinical Setting

The goal of this monocentric observational study involving hospitalised patients is to assess the integration of activity sensors into routine clinical practice.

Study Overview

• Status: Completed
• Conditions: Physical Inactivity; Hospitalized Patients

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.

• Study Type: Observational
• Enrollment (Actual): 19

Contacts and Locations

Study Locations

• Switzerland
  • Canton of Basel-City
    • Basel, Canton of Basel-City, Switzerland, 4031
      • Universitiy Hospital Basel, Division of Internal Medicine

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Probability Sample

Study Population

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.

Description

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

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	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

Secondary Outcome Measures

Outcome Measure	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
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
Evaluation of the handling	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)
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)

Collaborators and Investigators

• Sponsor: University Hospital, Basel, Switzerland
• Collaborators: Zurich University of Applied Sciences
• Investigators: Principal Investigator: Joris Kirchberger, University Hospital, Basel, Switzerland; Study Chair: Jens Eckstein, Prof. Dr. med., University Hospital, Basel, Switzerland

Study record dates

Study Major Dates

• Study Start (Actual): November 25, 2024
• Primary Completion (Actual): March 11, 2025
• Study Completion (Actual): June 30, 2025

Study Registration Dates

• First Submitted: January 9, 2025
• First Submitted That Met QC Criteria: January 16, 2025
• First Posted (Actual): January 20, 2025

Study Record Updates

• Last Update Posted (Estimated): October 1, 2025
• Last Update Submitted That Met QC Criteria: September 25, 2025
• Last Verified: September 1, 2025

More Information

Terms related to this study

• Keywords: Movement detection; Activity sensors; Algorithm development and validation; Hospital implementation
• Additional Relevant MeSH Terms: Behavior; Sedentary Behavior
• Other Study ID Numbers: 2024-002078; am24Eckstein3

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No