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Trial registered on ANZCTR

Registration number

ACTRN12618001635257

Ethics application status

Approved

Date submitted

21/09/2018

Date registered

3/10/2018

Date last updated

16/12/2020

Date data sharing statement initially provided

6/09/2019

Date results information initially provided

16/12/2020

Type of registration

Prospectively registered

Titles & IDs

Public title

Measuring 24-hour movement patterns in children and teenagers: the TIME-2-MOVE study.

Scientific title

Using novel technology to improve the measurement and evaluation of 24-hour movement patterns in children and teenagers.

Secondary ID [1]

Nil known.

Universal Trial Number (UTN)

n/a

Trial acronym

TIME-2-MOVE

'Technology to Improve the Measurement of 24-Hour Movement'

Linked study record

n/a

Health condition

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

Measurement of movement behaviours

Obesity

Condition category

Condition code

Public Health

Other public health

Diet and Nutrition

Obesity

Intervention/exposure

Study type

Observational

Patient registry

False

Target follow-up duration

Target follow-up type

Description of intervention(s) / exposure

TIME-2-MOVE is a 3-day validation study to determine the ability of the AX3 (an open-source accelerometer) and the Actigraph (arguably the most widely used accelerometer in children) to measure activity and sleep behaviours across the full 24-hour day (sleep, wake after sleep onset, sedentary, and light, moderate and vigorous activity) in relation to criterion measures of each behaviour (polysomnography for sleep, direct observation and video from wearable cameras for physical activity and sedentary behaviour).

The following will be used as our criterion measures:

Sleep: home-based polysomnography (PSG) will be performed over one study night within the 3-day study period. A fundamental aspect of this is the ability to identify sleep-wake transitions, which is traditionally challenging with accelerometers.

Physical activity and sedentary behaviour (lab setting): direct observation of children participating in a variety of structured activities in a university laboratory (e.g. running on a treadmill) will be performed over a 2-hour period within two weeks of a participant's 3-day free-living study period.

Physical activity and sedentary behaviour (free-living): criterion measures of free-living activity will be obtained over 3 days through the use of wearable Go Pro video cameras (worn viewing outwards on the chest). Participants will wear the cameras for up to 8 hours during waking hours only, and will have the opportunity to remove them whenever they wish, and in other specific circumstances. Video will provide an objective measure of real-world activities that occur throughout the day (e.g. running around a playground with other kids), allowing for direct comparison with accelerometer data.

We will use pattern recognition techniques to combine all this relevant data, so as to develop algorithms for assessing 24-hour movement patterns in the ‘real world’.

Intervention code [1]

Not applicable

Comparator / control treatment

The following gold standard comparators will be used:

Portable polysomnography to measure sleep and wake: Electrodes attached to the child measure brain wave activity, eye movements, and muscle activity, and indicate what stage of sleep the child is in and when waking occurs. Cardio-respiratory patterns (ECG) and oxygen levels (pulse oximetry) are also obtained.

Direct observation to measure physical activity and sedentary behaviour under controlled conditions: Participants are videoed undertaking 12 semi-structured activities that align with accepted definitions of sedentary, light, moderate and vigorous activity. Each child will be asked to perform each activity for 5 minutes in a set order, sitting quietly for 4 minutes between activities.

Wearable video cameras to measure physical activity and sedentary behaviour under free-living conditions: a Go Pro will be attached using a chest strap and will be worn for up to eight hours over the course of three study days. Extensive guidelines will be followed to ensure that ethical obligations are met, including those that centre around privacy issues. Participants/parents can view the videos and delete any segments that they wish before the research team sees them. Video segments will be coded into behaviours/postures of interest (e.g. lying, sitting, walking, running, dynamic movement, sleep, etc.).

Control group

Active

Outcomes

Primary outcome [1]

Balanced accuracy of the AX3 accelerometer for correctly classifying each epoch as sleep, wake, sedentary, light, moderate, and vigorous activity, assessed by comparing AX3 accelerometer data with overnight polysomnography (sleep, wake), direct observation and wearable camera data (sedentary, light, moderate, vigorous activity).

Timepoint [1]

Data assessed i) minute by minute over one night of sleep (6-12 hours) for the polysomnography, ii) over a two-hour laboratory session using direct observation, and iii) over up to 8 hours of wearable camera videos (during waking hours only).

Primary outcome [2]

Balanced accuracy of the Actigraph accelerometer for correctly classifying each epoch as sleep, wake, sedentary, light, moderate, and vigorous activity, assessed by comparing Actigraph accelerometer data with overnight polysomnography (sleep, wake), direct observation and wearable camera data (sedentary, light, moderate, vigorous activity).

Timepoint [2]

Secondary outcome [1]

Balanced accuracy of the AX3 and Actigraph accelerometers for correctly identifying sleep and wake assessed by comparing accelerometer data with overnight polysonography.

Timepoint [1]

Data assessed minute by minute over one night of sleep (6-12 hours).

Secondary outcome [2]

Balanced accuracy of the AX3 and Actigraph accelerometers for correctly identifying time in sedentary behaviour as assessed by comparing accelerometer data against direct observation and camera videos.

Timepoint [2]

Data assessed minute by minute over one two-hour laboratory session of direct observation, and up to 8 hours of wearable camera videos (during waking hours only).

Secondary outcome [3]

Balanced accuracy of the AX3 and Actigraph accelerometers for correctly identifying time in physical activity at different intensities (light, moderate, vigorous) as assessed by comparing AX3 accelerometer data against direct observation and camera videos.

Timepoint [3]

Data assessed minute by minute over one two-hour laboratory session of direct observation, and up to 8 hours of wearable camera videos (during waking hours only).

Eligibility

Key inclusion criteria

Key inclusion criteria:
• Those between the ages of 8 and 16 years (inclusive);
• Those who reside in the greater Dunedin area (the NZ city where the study is taking
place);
• Those with 'normal' sleep patterns*

*Children and teens with a wide range of ‘normal’ sleep patterns will be recruited, as distinguished from abnormal sleep via the use of a validated questionnaire.

Minimum age

8 Years

Maximum age

16 Years

Sex

Both males and females

Can healthy volunteers participate?

Yes

Key exclusion criteria

Key exclusion criteria:
• Sleep disorder (as identified by the SDSC*);
• Chronic medical condition or physical disability that impedes participation in physical
activity, or that may otherwise interfere with data collection.

*The Sleep Disturbance Scale for Children, or SDSC (which has been validated in this age group).

Study design

Purpose

Natural history

Duration

Cross-sectional

Selection

Defined population

Timing

Prospective

Statistical methods / analysis

Although we will be able to analyse accelerometry data using counts (the traditional method), our main analyses will use machine learning. Raw accelerometry data yields a vast array of information (100+ ‘features’ for each 5-second window) unlike count-based models which essentially provide 1 feature i.e. the count). In machine learning, this rich dataset uses known timeframes for specific activities (e.g. periods of sleep) from the criterion measures to develop algorithms that ‘recognise’ when each specified activity is taking place in the accelerometry data. The more information that can be provided (e.g. from the PSG, lab session, wearable cameras, multi sites, skin temperature sensors, heart rate), the more the models are able to recognise hidden patterns among the input features that predict the outcome variable, without being explicitly programmed where to look. We will use a random forest classifier.

The sample of 160 children will be randomised, stratified by age and sex, to development and test samples containing at least 60 children each (allowing for 25% dropout). The model will be generated in the development sample using grouped k-fold cross validation. This process splits the data into k folds, each time leaving out a different group of participants. The data are trained using the included participants, then tested in those left out. By repeating this process over and over, each time using a different ‘fold’, the overall performance of the model can be determined by averaging the results. Many studies finish here, but are then uncertain about the wider generalisability of their results. By having a further test sample, we can see how the final optimal model performs in a separate sample of children who have not been part of the development. The sensitivity (e.g. proportion of sleep episodes correctly identified as sleep), specificity (e.g. proportion of non-sleep episodes correctly identified as non-sleep), and balanced accuracy (mean of sensitivity and specificity) of specific activities of interest and overall 24-hour movement patterns will be calculated in the test sample. Using a SD for sensitivity of 6.5% for the least accurate behaviour (walking) in a lab-based setting,10 increased to 10% to allow for greater variation in a free-living environment, indicates a sample of 60 will have sufficient power (80%, alpha < 0.05) to detect sensitivity to a 95% precision level of ± 2.6%.

Recruitment

Recruitment status

Completed

Date of first participant enrolment

Anticipated

8/10/2018

Actual

3/03/2019

Date of last participant enrolment

Anticipated

30/04/2020

Actual

20/11/2019

Date of last data collection

Anticipated

15/05/2020

Actual

26/11/2019

Sample size

Target

160

Accrual to date

Final

136

Recruitment outside Australia

Country [1]

New Zealand

State/province [1]

Otago

Funding & Sponsors

Funding source category [1]

University

Name [1]

University of Otago

Address [1]

PO Box 56
Dunedin 9010

Country [1]

New Zealand

Primary sponsor type

Individual

Name

Rachael Taylor

Address

Department of Medicine
University of Otago
PO Box 56
Dunedin 9010

Country

New Zealand

Secondary sponsor category [1]

None

Name [1]

Address [1]

Country [1]

Ethics approval

Ethics application status

Approved

Ethics committee name [1]

University of Otago Human Ethics Committee (Health)

Ethics committee address [1]

Attn: Gary Witte
Manager, Academic Committees
Room G22 Clocktower Building
University of Otago
Dunedin, 9016
New Zealand

Ethics committee country [1]

New Zealand

Date submitted for ethics approval [1]

11/06/2018

Approval date [1]

22/06/2018

Ethics approval number [1]

H18/073

Summary

Brief summary

What does this project aim to do?
One in three New Zealand children are overweight or obese, with Maori, Pacific, and those from more deprived households being disproportionately affected. Three key contributing behaviours are low levels of physical activity, excess time spent sedentary, and inadequate sleep.

Although the health effects of these behaviours have traditionally been assessed in isolation, the consideration of all movement behaviours within a 24-hour period (i.e. sleep, sedentary, light, moderate, and vigorous activity) is shaping a fast-emerging field in health research called time-use epidemiology.

This shift is clearly illustrated by new activity guidelines for children – recently replicated in NZ – that address all movement behaviours across the 24-hour day. Measuring adherence to these guidelines presents challenges though, as traditional techniques were not designed to capture and evaluate 24-hour movement patterns.

In this study, we aim to figure out whether accelerometers (small motion sensors that are worn on the skin) are a simple and complete tool for measuring movement over the 24-hour period in children and teens.

Who will take part?
160 children and teenagers between the ages of 8-16 from the greater Dunedin area, in New Zealand.

Participants involved in this study will be asked to:
o Wear five accelerometers (at multiple sites, including their wrist, hip, thigh, and lower back) for three days and three nights, including while they sleep.
o Wear a video camera over the course of three days, just while they’re awake. It shows us what different activities they might be doing throughout their day.
o Wear a sleep-monitoring device for one night at home.
o Take part in some fun activity monitoring in a university lab environment.

Research Impact:
Important benefits should arise from this work. More accurate measurement of key behaviours should provide much-needed insight into how 24-hour movement patterns influence obesity and health, and perhaps explain inconsistencies across ethnic and sociodemographic groups.

Most importantly, despite the existence of new 24-hour activity guidelines, simple tools for measurement and analysis do not exist, and there is considerable international interest in their development.

Trial website

Trial related presentations / publications

Public notes

Attachments [1]

/AnzctrAttachments/375922-Ethics Decision Letter.pdf (Ethics approval)

Contacts

Principal investigator

Name

Prof Rachael Taylor

Address

Department of Medicine (DSM)
Dunedin School of Medicine
University of Otago
PO Box 56
Dunedin 9054
New Zealand

Country

New Zealand

Phone

+64 21 479 556

Fax

[email protected]

Contact person for public queries

Name

Prof Rachael Taylor

Address

Department of Medicine (DSM)
Dunedin School of Medicine
University of Otago
PO Box 56
Dunedin 9054
New Zealand

Country

New Zealand

Phone

+64 21 479 556

Fax

[email protected]

Contact person for scientific queries

Name

Prof Rachael Taylor

Address

Department of Medicine (DSM)
Dunedin School of Medicine
University of Otago
PO Box 56
Dunedin 9054
New Zealand

Country

New Zealand

Phone

+64 21 479 556

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

This information is to remain confidential on an individual basis.

What supporting documents are/will be available?

Doc. No.	Type	Citation	Link	Email	Other Details	Attachment
1757	Ethical approval				Ethics approval letter for proposed change to prot... [More Details]	375922-(Uploaded-29-03-2019-10-37-35)-Study-related document.pdf

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

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