ZeroFall - Reliability Testing of Optical Sensor to Detec... | NCT03901976

Studies
ZeroFall - Reliability Testing of Optical Sensor to Detect Bed Exit for Patients in Hospital

NCT03901976

Sponsor

Philips Healthcare

Status

Terminated

Last Update Posted

Nov 20, 2020Actual

Enrollment

14Actual

Phase

Not Applicable

Conditions

Delirium of Mixed Origin

Interventions

bed exit detection

Countries

United Kingdom

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

Status Module

Record Verification Date

Nov 2020

Overall Recruitment Status or Expanded Access Status

Terminated

Last Known Status

Not provided

Delayed Posting

Not provided

Why Stopped

Not enough resources in place; COVID-19 pandemic impedes working on the project and travelling within the EU

Expanded Access Info

Start Date

Mar 25, 2019Actual

Primary Completion Date

Nov 5, 2020Actual

Completion Date

Nov 5, 2020Actual

First Submitted Date

Mar 28, 2019

First Submission Date that Met QC Criteria

Apr 2, 2019

First Posted Date

Apr 3, 2019Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

Nov 18, 2020

Last Update Posted Date

Nov 20, 2020Actual

Oversight Module

Has Data Monitoring Committee (DMC)

Yes

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

Not provided

FDAAA801 Violation

Not provided

Description Module

Brief Summary

Falls are one of the most common NHS adverse events. With an increasing number of frail elderly patients being admitted this risk is likely to increase. In order to be able to assist patients with bed exit in a timely manner monitoring might be of help. In ZeroFall we will test the reliability of monitoring devices to notify care givers if a patient is attempting to exit the bed.

Detailed Description

In ZeroFall we intend to observe patients on risk of fall with two different devices during their hospital stay: The information from a no-touch optical sensing device that analyses movement and an under the mattress sensor that has already been used in a previous study at Bangor. Both are compatible with an existing monitoring system by Philips Healthcare and CE marked.

Design Module

Study Type

Interventional

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Not provided

Target Follow-Up Duration

Not provided

Phases

Not Applicable

Interventional Study Design

Allocation

Biospecimen

No data available

No data is available for this block.

Enrollment

14Actual

Arms/Interventions Module

Arm Groups

Label	Type	Description	Intervention Names
Bed Exit Detection On	Other	prevention of fall by a true bed exit detection	Device: bed exit detection

Interventions

Name	Type	Description	Arm Group Labels	Other Names
bed exit detection	Device	bed exit detection to avoid patients fall	Bed Exit Detection On

Outcomes Module

Primary Outcomes

Measure	Description	Time Frame
Number of true bed exit detections vs number of false bed exit detections	A reference device and two observers will classify if a bed exit was true	12-15 months

Secondary Outcomes

Not provided

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

Inclusion Criteria:

Adult patients with risk of fall more than 24 hours on the ward

Exclusion Criteria:

Patients with predominantly palliative needs

Accepts Healthy Volunteers

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

18 Years

Maximum Age

Not provided

Standard Ages

AdultOlder Adult

Study Population

Not provided

Sampling Method

Not provided

Contacts/Locations Module

Central Contacts

Not provided

Overall Officials

Name	Affiliation	Role
Christian P Subbe, MD	Ysbyty Gwynedd Hospital	Principal Investigator

Locations

Facility	Status	City	State	ZIP	Country	Contacts
Ysbyty Gwynedd		Bangor	Wales	LL57 2PW	United Kingdom

IPD Sharing Statement Module

Plan to Share IPD

Description

Not provided

Types

Not provided

Time Frame

Not provided

Access Criteria

Not provided

URL

Not provided

Condition Browse Module

MeSH Terms

ID	Term
D003693	Delirium

Ancestor Terms

ID	Term
D003221	Confusion
D019954	Neurobehavioral Manifestations
D009461	Neurologic Manifestations
D009422	Nervous System Diseases

Browse Leaves

Not provided

Browse Branches

Not provided

Intervention Model Description

The study has several distinct phases:

Silent Phase: The devices run in the background, activity is blinded for clinicians, settings are optimized by the research team.
Run-In Phase: Bed exit notifications are turned on, notifications will be configurable for acceptability by clinical teams. Data extraction and data evaluation will be tested.
Phase I: Data collection of bed exit data of sensors as well as data related to falls
Phase II: Data collection of bed exit data with optimization of predictive algorithms
Phase III: Data collection for validation using predictive data for early notification