Observational studyCritical care medicine2017
Measuring Patient Mobility in the ICU Using a Novel Noninvasive Sensor.
Observational study in Critical care medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Actigraphy to Evaluate Sleep in the Intensive Care Unit. A Systematic Review.Annals of the American Thoracic Society · 2018Pooled it
- Ambient intelligence-based framework for quantitative evaluation of cell culture operations to support standardization and training.Regenerative therapy · 2026Article
- A multimodal vision dataset for nursing action recognition and quality assessment in NICU.Scientific data · 2026Article
- Application of Artificial Intelligence in Physical Rehabilitation of Patients Admitted to the Intensive Care Unit: A Scoping Review.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2025Article
- Developing ICU Clinical Behavioral Atlas Using Ambient Intelligence and Computer Vision.NEJM AI · 2025Article
- Deep learning to quantify care manipulation activities in neonatal intensive care units.NPJ digital medicine · 2024Article
- Recent advances in research, training and teaching in anaesthesia and critical care.Indian journal of anaesthesia · 2023Article
- An Edge Computing and Ambient Data Capture System for Clinical and Home Environments.Sensors (Basel, Switzerland) · 2022Article
- Potentials and Challenges of Pervasive Sensing in the Intensive Care Unit.Frontiers in digital health · 2022Review
- Standardisation, multi-measure, data quality and trending: A qualitative study on multidisciplinary perspectives to improve intensive care early mobility monitoring.Intensive & critical care nursing · 2021Article
- Deep learning-enabled medical computer vision.NPJ digital medicine · 2021Review
- Review
- Quantifying Mobility in the ICU: Comparison of Electronic Health Record Documentation and Accelerometer-Based Sensors to Clinician-Annotated Video.Critical care explorations · 2020Article
- Recent advances in the technology of anesthesia.F1000Research · 2020Review
- CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer Assisted Interventions.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2020Article
- Intelligent ICU for Autonomous Patient Monitoring Using Pervasive Sensing and Deep Learning.Scientific reports · 2019Observational
- International Classification of Functioning, Disability, and Health Domains of 60 Physical Functioning Measurement Instruments Used During the Adult Intensive Care Unit Stay: A Scoping Review.Physical therapy · 2019Article
- A computer vision system for deep learning-based detection of patient mobilization activities in the ICU.NPJ digital medicine · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
objectivesTo develop and validate a noninvasive mobility sensor to automatically and continuously detect and measure patient mobility in the ICU.
designProspective, observational study.
settingSurgical ICU at an academic hospital. PATIENTS: Three hundred sixty-two hours of sensor color and depth image data were recorded and curated into 109 segments, each containing 1,000 images, from eight patients.
interventionsNone. MEASUREMENTS AND MAIN
resultsThree Microsoft Kinect sensors (Microsoft, Beijing, China) were deployed in one ICU room to collect continuous patient mobility data. We developed software that automatically analyzes the sensor data to measure mobility and assign the highest level within a time period. To characterize the highest mobility level, a validated 11-point mobility scale was collapsed into four categories: nothing in bed, in-bed activity, out-of-bed activity, and walking. Of the 109 sensor segments, the noninvasive mobility sensor was developed using 26 of these from three ICU patients and validated on 83 remaining segments from five different patients. Three physicians annotated each segment for the highest mobility level. The weighted Kappa (κ) statistic for agreement between automated noninvasive mobility sensor output versus manual physician annotation was 0.86 (95% CI, 0.72-1.00). Disagreement primarily occurred in the "nothing in bed" versus "in-bed activity" categories because "the sensor assessed movement continuously," which was significantly more sensitive to motion than physician annotations using a discrete manual scale.
conclusionsNoninvasive mobility sensor is a novel and feasible method for automating evaluation of ICU patient mobility.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.