ANZCTR - Registration

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Trial registered on ANZCTR

Registration number

ACTRN12617000981325

Ethics application status

Approved

Date submitted

29/06/2017

Date registered

7/07/2017

Date last updated

9/12/2020

Date data sharing statement initially provided

29/07/2019

Date results information initially provided

29/07/2019

Type of registration

Prospectively registered

Titles & IDs

Public title

Investigating a novel technological solution to prevent falls in older people in hospital

Scientific title

Effectiveness of an Ambient Intelligence Geriatric Management system (AmbIGeM) to prevent falls in older people in hospitals: the AmbIGeM stepped wedge pragmatic trial.

Secondary ID [1]

National Health and Medical Research Council Project grant: ID APP1082197.

Universal Trial Number (UTN)

Trial acronym

AmbIGeM

Linked study record

Health condition

Health condition(s) or problem(s) studied:

accidental falls

Condition category

Condition code

Injuries and Accidents

Other injuries and accidents

Public Health

Health service research

Intervention/exposure

Study type

Interventional

Description of intervention(s) / exposure

AmbIGeM intervention involves patients wearing a Bluetooth Low Energy (BLE) device with integrated sensors. The device positioned in a singlet pocket transmits movement signals, to a base station. Signals are interpreted by software that identifies risk circumstances and responds ‘intelligently’ (tailored response) to patient movements that lead to situations of increased falls risk. When a risk circumstance is identified by the system, clinical staff will be alerted via a hand held mobile device (vibration and/or alarm). Staff may then intervene and supervise the patient.
1. Wearable device and singlet. The device used in the trial has a number of inertial sensors (e.g. tri-axial accelerometer) and sensors for measuring elevation above ground. It is low cost and powered by a replaceable coin sized cell battery (typical battery life is 30 days). The device will sit in a plastic encasing to protect the device, improve comfort, and ensure the sensor is correctly orientated. The encasing has a black side and white side allowing nurses to easily determine the correct placement. A nurse will place the device within an ‘envelope type’ pocket on the inner side of a singlet worn by the patient under their hospital/own clothing, at the sternum position. Singlets will be changed as needed. The wearable device will be used one per patient, then disposed of. Lost or misplaced sensors will be replaced. A further device in an encasing will be attached to patient walking aids, for patients who are classified as not requiring supervision for transfers out of bed and chair but being risk takers who are unsafe in mobilizing without their walking aid during the admission. Wearable devices will capture patient physical orientation and movement information and wirelessly transmit this data. Different movements between the wearable device and the device attached to walking aids will allow detection that the walking aid is being used (or not) by the patient.
2. The modified hospital room environments and monitoring systems. To capture the data from wearable devices worn by patients and devices on walking aids, small form factor BLE enabled single board computer based listening devices (base stations) are installed on ceiling locations above each bed and door exit. This will provide adequate coverage for various configurations of one, two and four bed rooms. Base stations will collect and pre-process data from wearable devices worn by patients and forward this data for analysis by the backend AmbIGeM software to identify risky movements. Each base station will communicate with backend AmbIGeM systems over a Local Area Network. Activity recognition algorithms in the backend systems will process and analyse the data from all the devices forwarded from the base stations and in the context of the personalized information entered on the system by the staff, determine whether or not an alarm should be triggered, in real-time. The backend AmbIGeM systems will alert clinical staff when an assessed patient is undertaking high falls risk activities.
3. The AmbIGeM system. An electronic interface will run on a mobile device. The AmbIGeM Mobile App will be executed on an Android smart phone. Using this App, nurses, at the beginning of every shift, will select patients allocated to their care, determine the movement circumstances of individual patients where there is a risk of falling and the App will record this information onto the AmbIGeM system where the information collected will be used: i) to generate and print an individualized falls risk poster for display by the bedside, which will also act as a visual aid for falls prevention; and ii) to activate an alarm when a movement pattern indicating the identified risk movement occurs. Clinical staff, such as nurses and physiotherapists, will carry the mobile device and be alerted if a patient is undertaking falls risk related movements.
Alert notifications from the AmbIGeM server monitoring patient activities will be sent over the existing Wi-Fi network and be received by the Mobile App. Staff carrying the Mobile App will then attend the patient with the aim to mitigate the risk of falling through timely supervision. The alert notification received will include i) identity of the patient (who); ii) physical location of the patient (where); iii) type of high-risk activity (what); and iv) timestamp of when the high-risk activity was detected (when). Risky movements that may be set to activate the alarm include: Sitting up from lying on the bed; Standing up from bed or chair; Walking (can be limited to a area); and Walking without a required gait aid (ie if a patient is identified as safe to walk with their frame, but is considered not safe without their frame, starts walking without the frame the system can alarm, through the gait aid being tagged). Staff will be able to deactivate the alarm from the same Mobile App. Staff will record if an incident was a false or true alarm and what they noted when they attended the patient. During the intervention period, the AmbIGeM system will replace other sensor alarms for all participants.
A dedicated desktop falls management application (AmbIGeM Desktop App) at the nurses station will provide the capability to: i) enrol patients in the trial by assigning a wearable device to a patient; ii) visualize real-time updates of patient activity and current alarm information as well as a log of past alarms for individual patients; ii) discharge patients or un-enrol patients as required; iii) provide a facility for nurse managers to alter alert settings including sensitivity of alerts; iv) visually observe when falls risk related movements need updating based on user defined expiry times; and v) provide warnings to replace the battery when a patient stay lasts longer than typical battery life.
Strategies to maximise fidelity include staff in-service (fall definition and staff reporting of falls), and staff training/protocols for specific key project activities (eg singlet and Mobile App use). Research staff will check the AmbIGeM Desktop App to ensure eligible patients are wearing the sensor. The system will detect when there is no sensor movement for patients enrolled in the study, potentially indicating failure in the system (eg sensor, battery, reader, computer) so that research staff can check and intervene. If the Mobile App risk movements are not updated within 24 hours, nurses will receive a reminder.
Ward 1 intervention commences after block 1 (control phase) for 3 x 25 week blocks (75 week intervention); Ward 2 intervention commences after block 2 for 2 x 25 week blocks (50 week intervention); and Ward 3 intervention commences after block 3 for 1 x 25 week blocks (25 week intervention).

Intervention code [1]

Prevention

Intervention code [2]

Treatment: Devices

Comparator / control treatment

For the duration of the study (control and intervention blocks), ward staff will continue with best practice within their current resources. TQEH and SCGH have in place routine best practice falls prevention activities consistent with Australian falls prevention guidelines for hospitals, including falls risk screening and assessment, environment assessment, implementation of interventions for identified risk factors, such as appropriate positioning of call bells and mobility aids, adequate lighting, bed / chair sensor alarms for patients with high risk of falling, reduced clutter and compliance with restraint policies.

Control group

Active

Outcomes

Primary outcome [1]

Falls rate, calculated as the number of falls divided by the number of participant bed days.
Falls will be collected from 3 sources: health systems computerized incident reports, daily enquiry of falls from ward team leader and hand searching of patient medical notes/electronic health records. Number of participant bed days of all participants on the trial wards.

Timepoint [1]

At 103 weeks after start of control period, falls rate will be calculated.

Secondary outcome [1]

Proportion of participants falling.
Falls will be collected from 3 sources: health systems computerized incident reports, daily enquiry of falls from ward team leader and hand searching of patient medical notes/electronic health records.

Timepoint [1]

At 103 weeks after start of control period, proportion of participants falling rate will be calculated

Secondary outcome [2]

Rate of injurious falls per 1000 participant bed days.
Falls will be collected from 3 sources: health systems computerized incident reports, daily enquiry of falls from ward team leader and hand searching of patient medical notes/electronic health records. Number of participant bed days of all participants on the trial wards.

Timepoint [2]

At 103 weeks after start of control, injurious falls rate will be calculated

Secondary outcome [3]

To determine the acceptability of the intervention with patients and clinical staff by:
Patient pre/post intervention surveys and interviews: of the patients who participate in the intervention, every 5th participant will be invited to participate in the pre- and post-intervention survey and every 7th participant (up to a maximum of 30 participants) will be invited to participate in a post intervention interview of their experience with the sensor system.
Clinical staff focus group: all clinical staff who work on each cluster during the intervention period will be eligible to be included. Focus group will seek information from clinical staff about their overall experience with AmbIGeM, their perspectives of the positive and negative aspects of AmbIGeM, the implementation of AmbIGeM, and any recommendations for improvements of the intervention.
Clinical staff survey: all clinical staff working in the three wards will be invited to complete a survey. The survey will explore users’ experience of the intervention, opinions on its acceptability and suggestions for improvement. Items for the survey will be developed from themes identified in the clinical staff focus group.
Staff can anonymously provide feedback about the study and technology into a feedback box located outside the ward.

Timepoint [3]

Patient pre/post intervention surveys and interviews from the start of the intervention period
Clinical staff focus group: conducted after at last 20 weeks from the start of the intervention period
Clinical staff survey: after the clinical staff focus groups

Secondary outcome [4]

To determine the safety of the intervention with patients and clinical staff by:
Patient pre/post intervention surveys and interviews: of the patients who participate in the intervention, every 5th participant will be invited to participate in the pre- and post-intervention survey and every 7th participant (up to a maximum of 30 participants) will be invited to participate in a post intervention interview of their experience with the sensor system.
Clinical staff focus group: all clinical staff who work on each cluster during the intervention period will be eligible to be included. Focus group will seek information from clinical staff about their overall experience with AmbIGeM, their perspectives of the positive and negative aspects of AmbIGeM, the implementation of AmbIGeM, and any recommendations for improvements of the intervention.
Clinical staff survey: all clinical staff working in the three wards will be invited to complete a survey. The survey will explore users’ experience of the intervention, opinions on its acceptability and suggestions for improvement. Items for the survey will be developed from themes identified in the clinical staff focus group.
Staff can anonymously provide feedback about the study and technology into a feedback box located outside the ward.

Timepoint [4]

Secondary outcome [5]

To determine hospital costs. Data collection for an economic analysis will include capital cost, installation cost, maintenance cost, sensor replacement costs, singlets (for wearing the sensor), the cost of responding to sensor alarms and technology support costs. The frequency of alerts and response time by staff will be recorded. Throughout the study period, a record of staffing levels including the use of additional staff will be maintained, as staffing constraints may contribute to increased falls rates. In-service training and advice requirements will also be noted. Where a fall has occurred, the injuries, laboratory and radiology investigations immediately following and related to the fall, and any surgical interventions to treat injury related to the fall will be recorded. Data will be obtained from the medical records, and costs of investigations and interventions will be obtained from the finance departments of participating hospitals.
Health systems data held by the State Departments of Health in SA and WA will be linked to the study inpatient data to inform pre-study ward, study ward and post-study ward types and lengths of hospital stay and discharge destination. Use of hospital services including rehabilitation, Emergency Department presentations and re-admissions will be collated to 3 months post-discharge from the study ward (with an extended analysis planned for 12 months post-discharge).

Timepoint [5]

At 103 weeks after start of control, hospital costs will be calculated and at 115 and 155 weeks, use of hospital services will be calculated

Secondary outcome [6]

To determine mortality to 3 months post-discharge from study wards (with an extended analysis planned for 12 months post-discharge)

Timepoint [6]

At 115 and 155 weeks after start of control period, mortality will be calculated

Secondary outcome [7]

To determine use of residential care to 3 months post-discharge from study wards.

Timepoint [7]

At 115 weeks after start of control period, residential care will be calculated

Eligibility

Key inclusion criteria

Patients aged 65 years and older, admitted to the three study wards

Minimum age

65 Years

Maximum age

No limit

Sex

Both males and females

Can healthy volunteers participate?

Key exclusion criteria

Receiving palliative treatment

Study design

Purpose of the study

Prevention

Allocation to intervention

Non-randomised trial

Procedure for enrolling a subject and allocating the treatment (allocation concealment procedures)

Allocation is not concealed.
A waiver and opt-out of consent was obtained for this study. Posters will be displayed in the wards and flyers will be placed on all patient bed side lockers for participants, family and or carers, to inform them and staff of the research activities and provide key contact details where they can seek further information.
The AmbIGeM study will be delivered across three wards in two hospitals in two states of Australia (South Australia and Western Australia). It will take 103 weeks to complete, with each ward crossing over from usual care/control to intervention every 25 weeks until all wards have experienced one or more time blocks exposed to the intervention. A three week period after the first 25 week control block is included concomitantly across all three sites to test the technology prior to the commencement of the first intervention period in SA. To aid in the implementation of the technology required for the intervention, the order that the wards will cross over from control to intervention was predefined as the single South Australian Geriatric Evaluation and Management Unit (GEMU) ward at The Queen Elizabeth Hospital first, followed by the smaller Western Australian GEMU ward, and finally the larger Western Australian General Medicine ward at the Sir Charles Gairdner Hospital.

Methods used to generate the sequence in which subjects will be randomised (sequence generation)

Masking / blinding

Open (masking not used)

Who is / are masked / blinded?

Intervention assignment

Other

Other design features

Clustered stepped wedge

Phase

Not Applicable

Type of endpoint/s

Safety/efficacy

Statistical methods / analysis

The statistical analysis will be blinded regarding the trial phase participants were involved in (intervention or control block). All analyses will be conducted using intention to treat principles. The primary outcome of the falls rate will be analysed using a Poisson generalised linear regression model including effects for intervention, ward and time period to account for the clustered, stepped-wedge design of the study. Patients recruited to the study during a control period will be censored when the ward transitions to the intervention, with falls and length of stay data only collected up until the time of transition. The secondary outcome of the rates of injurious falls will be analysed similarly. All Poisson models will be examined for over- and under-dispersion. The proportion of participants falling will be analysed by binary logistic regression, accounting for ward and time period effects. The Charlson Comorbidity Index will be used as a covariate to adjust analyses for baseline differences between the control and intervention groups. A subgroup analysis of the effect of the intervention within patients with and without dementia will also be conducted, by including the presence of dementia diagnosis and delirium diagnosis at discharge as an interaction effect in a secondary model. For all analyses significance will be determined at the 5% level. No imputation of missing outcome data will be conducted.
Health economic analysis. The cost-effectiveness analysis will include patient-level cost estimates comprising intervention costs (estimated as fixed and variable costs, converted to a daily equivalent cost, with the fixed costs annuitized over the expected lifetime of the intervention); daily ward costs (allocated by dividing the aggregate daily ward cost, based on recorded staffing levels, by the number of patients on the ward on each day; and costs relating to falls, based on recorded hospital activity for eligible patients (e.g. imaging and surgical procedures). Costs incurred in the three-month period post-discharge from the study ward will represent time spent on other hospital wards, the receipt of other hospital-based services (e.g. home-based rehabilitation services) and residential care costs incurred by the Australian Federal and/or State governments using discharge destination data.
Within the trial, cost-effectiveness will be represented as the incremental cost per fall avoided, per fall-free separation, and per avoidance of discharge to residential care. Extrapolation of the longer-term costs and consequences of observed differences in reported outcomes will be considered, for example, with reference to models of the long-term costs and effects of frailty.
Safety and acceptability analysis. Two members of the research team will independently undertake analysis of data pertaining to assessing the intervention acceptability and safety. Text data from interviews and focus groups will be transcribed. Transcriptions will be read multiple times to obtain a sense of the whole meanings. Notes taken during meetings will help to clarify discussion points in the recordings. Data from anonymous feedback boxes will be collated and added to the transcriptions. Date will be analysed using NVivoTM software.
A process of Open Coding where focus group data and text data from surveys is marked and labelled with a code describing its content will be undertaken. Once the initial codes are generated, they will be discussed and analysed to form ‘nodes’. The ‘nodes’ relating to each other will be grouped together, forming ‘concepts’. ‘Concepts’ that have similar meaning will be merged together forming the ‘categories’. The main questions used in the interview guide will be used to label the main categories as this allows reporting of participants’ responses to individual questions separately. Separate categories may be created for the coded content that doesn’t fall under the main questions.
Numerical data from patient and clinical staff surveys will be analysed using descriptive frequencies and integrated into the focus group and interview data analysis to provide an in-depth analysis of the feasibility and acceptability of the intervention.

Recruitment

Recruitment status

Completed

Date of first participant enrolment

Anticipated

10/07/2017

Actual

10/07/2017

Date of last participant enrolment

Anticipated

30/06/2019

Actual

30/06/2019

Date of last data collection

Anticipated

28/06/2020

Actual

30/06/2019

Sample size

Target

2400

Accrual to date

Final

3252

Recruitment in Australia

Recruitment state(s)

SA,WA

Recruitment hospital [1]

The Queen Elizabeth Hospital - Woodville

Recruitment hospital [2]

Sir Charles Gairdner Hospital - Nedlands

Recruitment postcode(s) [1]

5011 - Woodville

Recruitment postcode(s) [2]

6009 - Nedlands

Funding & Sponsors

Funding source category [1]

Government body

Name [1]

National Health and Medical Research Council

Address [1]

Research Committee Secretariat NHMRC
GPO Box 1421 Canberra ACT 2601

Country [1]

Australia

Primary sponsor type

University

Name

The University of Adelaide

Address

GPO Box 498
ADELAIDE SA 5001

Country

Australia

Secondary sponsor category [1]

None

Name [1]

Address [1]

Country [1]

Other collaborator category [1]

Individual

Name [1]

Professor Keith Hill

Address [1]

School of Physiotherapy and Exercise Science
Curtin University
GPO Box U1987
Perth WA 6102

Country [1]

Australia

Other collaborator category [2]

Individual

Name [2]

Dr Damith Ranasinghe

Address [2]

School of Computer Science
University of Adelaide
Adelaide SA 5005

Country [2]

Australia

Other collaborator category [3]

Individual

Name [3]

Ms Kylie Lange

Address [3]

Discipline of Medicine
University of Adelaide
Adelaide SA 5005

Country [3]

Australia

Other collaborator category [4]

Individual

Name [4]

Professor Anne Wilson

Address [4]

Adelaide Geriatrics Training and Research with Aged Care (GTRAC) Centre
Adelaide Medical School
Faculty of Health and Medical Sciences
University of Adelaide
28 Woodville Road
Woodville South, SA 5011; and

School of Medicine
Flinders University of South Australia
Registry Road
Bedford Park SA 5042

Country [4]

Australia

Other collaborator category [5]

Individual

Name [5]

Mr Stephen Hoskins

Address [5]

Aged & Extended Care Services
The Queen Elizabeth Hospital
28 Woodville Road
Woodville South SA 5011

Country [5]

Australia

Other collaborator category [6]

Individual

Name [6]

Dr Pazhoor Shibu

Address [6]

Aged & Extended Care Services
The Queen Elizabeth Hospital
28 Woodville Road
Woodville South SA 5011

Country [6]

Australia

Other collaborator category [7]

Individual

Name [7]

Dr Sean Maher

Address [7]

Department of Rehabilitation and Aged Care
Sir Charles Gairdner Hospital
Nedlands WA 6009

Country [7]

Australia

Other collaborator category [8]

Individual

Name [8]

Dr Kate Ingram

Address [8]

Department of Rehabilitation and Aged Care
Sir Charles Gairdner Hospital
Nedlands WA 6009

Country [8]

Australia

Other collaborator category [9]

Individual

Name [9]

Mr Ian Cooper

Address [9]

Physiotherapy Department
Sir Charles Gairdner Hospital
Nedlands WA 6009

Country [9]

Australia

Other collaborator category [10]

Individual

Name [10]

Professor Jonathan Karnon

Address [10]

Discipline of Public Health
University of Adelaide
Adelaide SA 5005

Country [10]

Australia

Other collaborator category [11]

Individual

Name [11]

Dr Joanne Dollard

Address [11]

Country [11]

Australia

Other collaborator category [12]

Individual

Name [12]

Ms Eileen Boyle

Address [12]

School of Physiotherapy and Exercise Science
Curtin University
GPO Box U1987
Perth WA 6102

Country [12]

Australia

Ethics approval

Ethics application status

Approved

Ethics committee name [1]

Human Research Ethics Committee (The Queen Elizabeth Hospital/Lyell McEwin Hospital /Modbury Hospital)

Ethics committee address [1]

The Queen Elizabeth Hospital
Ethics: DX465101
Ground Floor, Basil Hetzel Institute
28 Woodville Road
WOODVILLE SOUTH SA 5011

Ethics committee country [1]

Australia

Date submitted for ethics approval [1]

Approval date [1]

06/04/2017

Ethics approval number [1]

HREC/15/TQEH/17

Ethics committee name [2]

Sir Charles Gairdner Hospital Human Research Ethics Committee

Ethics committee address [2]

Level 2 A Block, Hospital Ave,
Nedlands WA 6009

Ethics committee country [2]

Australia

Date submitted for ethics approval [2]

Approval date [2]

27/05/2017

Ethics approval number [2]

HREC No: 2015-110

Summary

Brief summary

Background: Although current best practice recommendations contribute to falls prevention in hospital, falls and injury rates remain high. There is a need to explore new interventions to reduce falls rates, especially in geriatric and general medical wards where older patients and those with cognitive impairment are managed.
Design and Methods: A 3-cluster stepped wedge pragmatic trial of the Ambient Intelligent Geriatric Management (AmbIGeM) (wearable sensor device to alert staff of patients undertaking at-risk activities) system for preventing falls in older patients compared to standard care. The trial will be conducted on three acute/subacute wards in two hospitals in Adelaide and Perth, Australia.
Participants: Patients aged >65 years admitted to study wards. A waiver and opt-out of consent was obtained for this study. Patients requiring palliative care will be excluded.
Outcomes: The primary outcome is falls rate; secondary outcome measures are: i) proportion of participants falling, ii) rate of injurious in-patient falls/1000 participant bed-days, iii) acceptability and safety of the interventions from patients and clinical staff perspectives, and iv) hospital costs, mortality and use of residential care to 3 months post-discharge from study wards.
Discussion: This study investigates a novel technological approach to preventing falls in hospitalised older people. We hypothesize that the AmbIGeM intervention will reduce falls and injury rates in participating wards, with an economic benefit attributable to the intervention. If successful, the AmbIGeM system will be a useful addition to falls prevention in hospital wards with high proportions of older people and people with cognitive impairment.

Trial website

Trial related presentations / publications

Public notes

Contacts

Principal investigator

Name

Prof Renuka Visvanathan

Address

Aged & Extended Care Services, The Queen Elizabeth Hospital, and
Adelaide Geriatrics Training and Research with Aged Care (GTRAC) Centre
Adelaide Medical School
Faculty of Health and Medical Sciences
University of Adelaide
28 Woodville Road
Woodville South, SA 5011

Country

Australia

Phone

+618 82226000

Fax

[email protected]

Contact person for public queries

Name

Prof Renuka Visvanathan

Address

Country

Australia

Phone

+618 82226000

Fax

[email protected]

Contact person for scientific queries

Name

Prof Renuka Visvanathan

Address

Country

Australia

Phone

+618 82226000

Fax

[email protected]

Data sharing statement

Will individual participant data (IPD) for this trial be available (including data dictionaries)?

No/undecided IPD sharing reason/comment

Informed consent for data sharing was not sought from participants

What supporting documents are/will be available?

Doc. No.	Type	Citation	Link	Email	Other Details	Attachment
2937	Study protocol	Visvanathan, R., Ranasinghe, D., Wilson, A., Lange, K., Dollard, J., Boyle, E., Karnon, J, Raygan, E, Maher, S, Ingram, K, Pazhvoor, S, Hoskins, S, & Hill, K. (2017). Effectiveness of an Ambient Intelligent Geriatric Management system (AmbIGeM) to prevent falls in older people in hospitals: protocol for the AmbIGeM stepped wedge pragmatic trial. Injury prevention: Journal of the International Society for Child and Adolescent Injury Prevention, injuryprev-2017-042507	https://injuryprevention.bmj.com/content/25/3/157
2938	Statistical analysis plan		https://health.adelaide.edu.au/medicine/g-trac/downloads/Statistical_analysis_plan_AMBIGEM_v3.pdf

Results publications and other study-related documents

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Documents added automatically

Source	Title	Year of Publication	DOI
Embase	Cost-Effectiveness and Value of Information Analysis of an Ambient Intelligent Geriatric Management (AmbIGeM) System Compared to Usual Care to Prevent Falls in Older People in Hospitals.	2023	https://dx.doi.org/10.1007/s40258-022-00773-6
Embase	Effectiveness of an Ambient Intelligent Geriatric Management system (AmbIGeM) to prevent falls in older people in hospitals: protocol for the AmbIGeM stepped wedge pragmatic trial.	2019	https://dx.doi.org/10.1136/injuryprev-2017-042507

N.B. These documents automatically identified may not have been verified by the study sponsor.

Trial Review

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