ANZCTR - Registration

Please note the ANZCTR will be unattended from Friday 20 December 2024 for the holidays. The Registry will re-open on Tuesday 7 January 2025. Submissions and updates will not be processed during that time.

Registering a new trial?

To achieve prospective registration, we recommend submitting your trial for registration at the same time as ethics submission.

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been endorsed by the ANZCTR. Before participating in a study, talk to your health care provider and refer to this information for consumers

Trial registered on ANZCTR

Registration number

ACTRN12619000811101p

Ethics application status

Not yet submitted

Date submitted

23/05/2019

Date registered

4/06/2019

Date last updated

4/06/2019

Date data sharing statement initially provided

4/06/2019

Type of registration

Prospectively registered

Titles & IDs

Public title

The feasibility of using the clinical health tracker app and web-interface to assess surgical risk in elective surgical patients.

Scientific title

The feasibility of using the clinical health tracker app and web-interface to assess peri-operative risk in elective surgical patients.

Secondary ID [1]

None

Universal Trial Number (UTN)

Trial acronym

Linked study record

Health condition

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

Perioperative risk in elective surgical patients

Condition category

Condition code

Anaesthesiology

Anaesthetics

Surgery

Other surgery

Intervention/exposure

Study type

Interventional

Description of intervention(s) / exposure

This study aims to assess whether a cross platform smartphone app, the Clinical Health Tracker App which reports the location of the participant's phone every 5 minutes and reports step data from HealthKit (iOS) and Google Fit (Android) when the phone has a Wi-Fi connection connection. The data reported from the app can be used to derive several measures that assess a patient's level of activity and participation, which represent a patient's level of disability. This information on disability could then potentially be used to predict surgical risk and outcome.

To use the Clinical Health Tracker app, patients must own a smartphone. Patients will be provided with information on how to download and activate the app before surgery and how to remove it once the data is collected (3 months after discharge from hospital). This app is free to download from the app store. This app will be downloaded at least 2 weeks prior to surgery.

The Clinical Health Tracker App does not require any training to use as it works in the background. There are surveys in the app that are activated by push activation that are used to reactivate the app should it stop sending data. Hence, the patient only needs to download the app onto their smartphone and leave the app on their phone- the app will report the step and GPS data automatically in the background for 3 months post surgery.

Intervention code [1]

Diagnosis / Prognosis

Intervention code [2]

Treatment: Devices

Comparator / control treatment

No control group

Control group

Uncontrolled

Outcomes

Primary outcome [1]

Feasibility of smartphone follow up
The number of patients who consent and use the app will be measured, as well as a a record the number of days of complete data for each participant.
This will be assessed by analysing data obtained from the app on an excel spreadsheet.

Timepoint [1]

3 months post surgery

Primary outcome [2]

Step count outcomes
Includes proportion of time spent inactive, daily step count, distance walked, average walking speed. This is a composite outcome
This will be assessed through summary data on an excel spreadsheet derived from the app.

Timepoint [2]

3 months post surgery

Primary outcome [3]

Hospital Outcomes
Number of days free from hospital, nursing home care or rehabilitation at 3 months and death during hospital stay and at 3 months. This is a composite outcome
This data will be collected through medical records

Timepoint [3]

3 months post surgery

Secondary outcome [1]

Patient satisfaction
Patient satisfaction with the use of a smartphone app for follow-up will be assessed with a survey designed specifically for this study

Timepoint [1]

3 months post surgery

Secondary outcome [2]

GPS outcomes
Includes time spent at home, linear distance travelled, number of locations visited, minimum convex polygon activity space (area of a polygon that bounds the GPS locations) and standard deviation ellipse activity space (the area of an ellipse plotted from the mean longitude and latitude). This is a composite outcome.
This will be assessed through summary data on an excel spreadsheet derived from the app.

NB: This is a Primary Outcome

Timepoint [2]

3 months post surgery

Eligibility

Key inclusion criteria

Undergoing elective surgery greater than 14 days from recruitment
Aged over 18
Own a smartphone

Minimum age

18 Years

Maximum age

No limit

Sex

Both males and females

Can healthy volunteers participate?

Key exclusion criteria

Refusal or inability to give informed consent
Undergoing cardiothoracic surgery

Study design

Purpose of the study

Treatment

Allocation to intervention

Non-randomised trial

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

Not applicable

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

Not applicable

Masking / blinding

Open (masking not used)

Who is / are masked / blinded?

Intervention assignment

Single group

Other design features

Phase

Not Applicable

Type of endpoint/s

Efficacy

Statistical methods / analysis

Continuous data will be described by mean (standard deviation) or median [interquartile range] depending upon normality of distribution. Categorical data will be summarised as number (percentage).

Where repeated measures exist, population average point estimates and between group comparisons will employ generalised estimating equations.
Concordance between like measures will be assessed by Lin’s concordance correlation statistic and Bland-Altman limits of agreement and correlation by Pearson or Spearman correlation coefficient, depending on appropriateness, and Bland-Altman Analysis.

Recruitment

Recruitment status

Not yet recruiting

Date of first participant enrolment

Anticipated

1/08/2019

Actual

Date of last participant enrolment

Anticipated

Actual

Date of last data collection

Anticipated

31/07/2020

Actual

Sample size

Target

500

Accrual to date

Final

Recruitment in Australia

Recruitment state(s)

Recruitment hospital [1]

The Royal Adelaide Hospital - Adelaide

Recruitment hospital [2]

Flinders Medical Centre - Bedford Park

Recruitment hospital [3]

Lyell McEwin Hospital - Elizabeth Vale

Recruitment postcode(s) [1]

5000 - Adelaide

Recruitment postcode(s) [2]

5042 - Bedford Park

Recruitment postcode(s) [3]

5112 - Elizabeth Vale

Funding & Sponsors

Funding source category [1]

Self funded/Unfunded

Name [1]

None

Address [1]

Not applicable

Country [1]

Primary sponsor type

Individual

Name

Samuel Gluck

Address

Intensive Care Unit
Royal Adelaide Hospital
1 Port Road
Adelaide
SA 5000

Country

Australia

Secondary sponsor category [1]

None

Name [1]

Address [1]

Country [1]

Ethics approval

Ethics application status

Not yet submitted

Ethics committee name [1]

Central Adelaide Local Health Network Human Research Ethics Committee

Ethics committee address [1]

Level 3, Roma Mitchell House
136 North Terrace, ADELAIDE SA 5000

Ethics committee country [1]

Australia

Date submitted for ethics approval [1]

30/06/2019

Approval date [1]

Ethics approval number [1]

Summary

Brief summary

Summary
The World Health Organisations International Classification of Function describes disability as the relationship between body function, activity levels and the level of participation. A patient’s level of disability has been shown to have adverse associations with surgical outcome. We have remotely measured patients’ activity and participation, continuously and objectively, using a cross platform smartphone application (app). The app reports step and Global Positioning System (GPS) data to a cloud database. Patients are generally accepting of this technology and show high levels of satisfaction with the process. We have used the data to generate meaningful outcomes for patients such as time spent at home and step counts. We have monitored patients prior to and following cardiothoracic surgery. 
These data will have a role in perioperative assessment. Currently perioperative assessment is performed using questionnaires and clinical assessment. These achieve a C-statistic for mortality prediction of 0.8-0.9.
Machine learning techniques will enable the smartphone data to generate more accurate risk models for patients undergoing surgery and provide prediction for long-term patient-centred outcomes.
Our objective is to assess the feasibility for a larger study. We intend to conduct a multicentre study enrolling 500 smartphone owning patients undergoing surgery at one of three centres in South Australia. Patients will receive a letter with instructions on how to install our app pre-operatively and be followed up for 90 days.
We will report the time taken to achieve ethics and governance approval at each site and the number of patients who successfully activate the app at least seven days prior to surgery. We will report on the completeness of the step and GPS data from the individuals enrolled. We will use the data to power a larger study to predict days free from hospital at 90-days, time spent at home, daily step count and time spent inactive at 90-days.



Plain English Statement
Increasingly patients own smartphones. The multitude of sensors held in these devices allow for remote monitoring of patients. We believe that by using these data in a machine learning model, we will improve the prediction of risk of surgery, and also provide data on outcomes that are more meaningful to patients. We intend to assess the feasibility of using a smartphone app to monitor patients prior to and following surgery.

Trial website

Trial related presentations / publications

Public notes

Contacts

Principal investigator

Name

Dr Samuel Gluck

Address

Intensive Care Unit
Royal Adelaide Hospital
1 Port Road
Adelaide
SA 5000

Country

Australia

Phone

+61 423 580 064

Fax

s.gluck@doctors.org.uk

Contact person for public queries

Name

Samuel Gluck

Address

Intensive Care Unit
Royal Adelaide Hospital
1 Port Road
Adelaide
SA 5000

Country

Australia

Phone

+61 423 580 064

Fax

s.gluck@doctors.org.uk

Contact person for scientific queries

Name

Samuel Gluck

Address

Intensive Care Unit
Royal Adelaide Hospital
1 Port Road
Adelaide
SA 5000

Country

Australia

Phone

+61 423 580 064

Fax

s.gluck@doctors.org.uk

Data sharing statement

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

No/undecided IPD sharing reason/comment

All participant data will be de-identified to comply with SA Health Confidentiality Guidelines

What supporting documents are/will be available?

No Supporting Document Provided

Results publications and other study-related documents

Documents added manually

No documents have been uploaded by study researchers.

Documents added automatically

No additional documents have been identified.

Trial Review

Registering a new trial?

Documents added manually

Documents added automatically