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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
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] 295975 0
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 309487 0
Obesity 309488 0
Condition category
Condition code
Public Health 308321 308321 0 0
Other public health
Diet and Nutrition 308580 308580 0 0
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] 312307 0
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] 307301 0
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] 307301 0
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] 307540 0
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] 307540 0
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).
Secondary outcome [1] 351564 0
Balanced accuracy of the AX3 and Actigraph accelerometers for correctly identifying sleep and wake assessed by comparing accelerometer data with overnight polysonography.
Timepoint [1] 351564 0
Data assessed minute by minute over one night of sleep (6-12 hours).
Secondary outcome [2] 352266 0
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] 352266 0
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] 352267 0
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] 352267 0
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] 20821 0
New Zealand
State/province [1] 20821 0
Otago

Funding & Sponsors
Funding source category [1] 300572 0
University
Name [1] 300572 0
University of Otago
Country [1] 300572 0
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] 300071 0
None
Name [1] 300071 0
Address [1] 300071 0
Country [1] 300071 0

Ethics approval
Ethics application status
Approved
Ethics committee name [1] 301361 0
University of Otago Human Ethics Committee (Health)
Ethics committee address [1] 301361 0
Ethics committee country [1] 301361 0
New Zealand
Date submitted for ethics approval [1] 301361 0
11/06/2018
Approval date [1] 301361 0
22/06/2018
Ethics approval number [1] 301361 0
H18/073

Summary
Brief summary
Trial website
Trial related presentations / publications
Public notes
Attachments [1] 3076 3076 0 0
/AnzctrAttachments/375922-Ethics Decision Letter.pdf (Ethics approval)

Contacts
Principal investigator
Name 86746 0
Prof Rachael Taylor
Address 86746 0
Department of Medicine (DSM)
Dunedin School of Medicine
University of Otago
PO Box 56
Dunedin 9054
New Zealand
Country 86746 0
New Zealand
Phone 86746 0
+64 21 479 556
Fax 86746 0
Email 86746 0
rachael.taylor@otago.ac.nz
Contact person for public queries
Name 86747 0
Rachael Taylor
Address 86747 0
Department of Medicine (DSM)
Dunedin School of Medicine
University of Otago
PO Box 56
Dunedin 9054
New Zealand
Country 86747 0
New Zealand
Phone 86747 0
+64 21 479 556
Fax 86747 0
Email 86747 0
rachael.taylor@otago.ac.nz
Contact person for scientific queries
Name 86748 0
Rachael Taylor
Address 86748 0
Department of Medicine (DSM)
Dunedin School of Medicine
University of Otago
PO Box 56
Dunedin 9054
New Zealand
Country 86748 0
New Zealand
Phone 86748 0
+64 21 479 556
Fax 86748 0
Email 86748 0
rachael.taylor@otago.ac.nz

Data sharing statement
Will individual participant data (IPD) for this trial be available (including data dictionaries)?
No
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.TypeCitationLinkEmailOther DetailsAttachment
1757Ethical 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.